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2025-07-14 00:00:00
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2024-04-22T00:00:00 | 2404.12547 | Does Gaussian Splatting need SFM Initialization? | [
"Yalda Foroutan",
"Daniel Rebain",
"Kwang Moo Yi",
"Andrea Tagliasacchi"
]
| 3D Gaussian Splatting has recently been embraced as a versatile and effective method for scene reconstruction and novel view synthesis, owing to its high-quality results and compatibility with hardware rasterization. Despite its advantages, Gaussian Splatting's reliance on high-quality point cloud initialization by Structure-from-Motion (SFM) algorithms is a significant limitation to be overcome. To this end, we investigate various initialization strategies for Gaussian Splatting and delve into how volumetric reconstructions from Neural Radiance Fields (NeRF) can be utilized to bypass the dependency on SFM data. Our findings demonstrate that random initialization can perform much better if carefully designed and that by employing a combination of improved initialization strategies and structure distillation from low-cost NeRF models, it is possible to achieve equivalent results, or at times even superior, to those obtained from SFM initialization. |
|
2024-04-22T00:00:00 | 2404.12833 | How Far Can We Go with Practical Function-Level Program Repair? | [
"Jiahong Xiang",
"Xiaoyang Xu",
"Fanchu Kong",
"Mingyuan Wu",
"Haotian Zhang",
"Yuqun Zhang"
]
| Recently, multiple Automated Program Repair (APR) techniques based on Large Language Models (LLMs) have been proposed to enhance the repair performance. While these techniques mainly focus on the single-line or hunk-level repair, they face significant challenges in real-world application due to the limited repair task scope and costly statement-level fault localization. However, the more practical function-level APR, which broadens the scope of APR task to fix entire buggy functions and requires only cost-efficient function-level fault localization, remains underexplored. In this paper, we conduct the first comprehensive study of LLM-based function-level APR including investigating the effect of the few-shot learning mechanism and the auxiliary repair-relevant information. Specifically, we adopt six widely-studied LLMs and construct a benchmark in both the Defects4J 1.2 and 2.0 datasets. Our study demonstrates that LLMs with zero-shot learning are already powerful function-level APR techniques, while applying the few-shot learning mechanism leads to disparate repair performance. Moreover, we find that directly applying the auxiliary repair-relevant information to LLMs significantly increases function-level repair performance. Inspired by our findings, we propose an LLM-based function-level APR technique, namely SRepair, which adopts a dual-LLM framework to leverage the power of the auxiliary repair-relevant information for advancing the repair performance. The evaluation results demonstrate that SRepair can correctly fix 300 single-function bugs in the Defects4J dataset, largely surpassing all previous APR techniques by at least 85%, without the need for the costly statement-level fault location information. Furthermore, SRepair successfully fixes 32 multi-function bugs in the Defects4J dataset, which is the first time achieved by any APR technique ever to our best knowledge. |
|
2024-04-22T00:00:00 | 2404.12753 | AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation | [
"Wenhao Huang",
"Chenghao Peng",
"Zhixu Li",
"Jiaqing Liang",
"Yanghua Xiao",
"Liqian Wen",
"Zulong Chen"
]
| https://github.com/EZ-hwh/AutoCrawler | Web automation is a significant technique that accomplishes complicated web tasks by automating common web actions, enhancing operational efficiency, and reducing the need for manual intervention. Traditional methods, such as wrappers, suffer from limited adaptability and scalability when faced with a new website. On the other hand, generative agents empowered by large language models (LLMs) exhibit poor performance and reusability in open-world scenarios. In this work, we introduce a crawler generation task for vertical information web pages and the paradigm of combining LLMs with crawlers, which helps crawlers handle diverse and changing web environments more efficiently. We propose AutoCrawler, a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding. Through top-down and step-back operations, AutoCrawler can learn from erroneous actions and continuously prune HTML for better action generation. We conduct comprehensive experiments with multiple LLMs and demonstrate the effectiveness of our framework. Resources of this paper can be found at https://github.com/EZ-hwh/AutoCrawler |
2024-04-22T00:00:00 | 2404.12872 | LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency | [
"Zhaodonghui Li",
"Haitao Yuan",
"Huiming Wang",
"Gao Cong",
"Lidong Bing"
]
| Query rewrite, which aims to generate more efficient queries by altering a SQL query's structure without changing the query result, has been an important research problem. In order to maintain equivalence between the rewritten query and the original one during rewriting, traditional query rewrite methods always rewrite the queries following certain rewrite rules. However, some problems still remain. Firstly, existing methods of finding the optimal choice or sequence of rewrite rules are still limited and the process always costs a lot of resources. Methods involving discovering new rewrite rules typically require complicated proofs of structural logic or extensive user interactions. Secondly, current query rewrite methods usually rely highly on DBMS cost estimators which are often not accurate. In this paper, we address these problems by proposing a novel method of query rewrite named LLM-R2, adopting a large language model (LLM) to propose possible rewrite rules for a database rewrite system. To further improve the inference ability of LLM in recommending rewrite rules, we train a contrastive model by curriculum to learn query representations and select effective query demonstrations for the LLM. Experimental results have shown that our method can significantly improve the query execution efficiency and outperform the baseline methods. In addition, our method enjoys high robustness across different datasets. |
|
2024-04-22T00:00:00 | 2404.12803 | TextSquare: Scaling up Text-Centric Visual Instruction Tuning | [
"Jingqun Tang",
"Chunhui Lin",
"Zhen Zhao",
"Shu Wei",
"Binghong Wu",
"Qi Liu",
"Hao Feng",
"Yang Li",
"Siqi Wang",
"Lei Liao",
"Wei Shi",
"Yuliang Liu",
"Hao Liu",
"Yuan Xie",
"Xiang Bai",
"Can Huang"
]
| Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data. To this end, we introduce a new approach for creating a massive, high-quality instruction-tuning dataset, Square-10M, which is generated using closed-source MLLMs. The data construction process, termed Square, consists of four steps: Self-Questioning, Answering, Reasoning, and Evaluation. Our experiments with Square-10M led to three key findings: 1) Our model, TextSquare, considerably surpasses open-source previous state-of-the-art Text-centric MLLMs and sets a new standard on OCRBench(62.2%). It even outperforms top-tier models like GPT4V and Gemini in 6 of 10 text-centric benchmarks. 2) Additionally, we demonstrate the critical role of VQA reasoning data in offering comprehensive contextual insights for specific questions. This not only improves accuracy but also significantly mitigates hallucinations. Specifically, TextSquare scores an average of 75.1% across four general VQA and hallucination evaluation datasets, outperforming previous state-of-the-art models. 3) Notably, the phenomenon observed in scaling text-centric VQA datasets reveals a vivid pattern: the exponential increase of instruction tuning data volume is directly proportional to the improvement in model performance, thereby validating the necessity of the dataset scale and the high quality of Square-10M. |
|
2024-04-22T00:00:00 | 2404.13013 | Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models | [
"Chuofan Ma",
"Yi Jiang",
"Jiannan Wu",
"Zehuan Yuan",
"Xiaojuan Qi"
]
| https://github.com/FoundationVision/Groma | We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability. Beyond holistic image understanding, Groma is adept at region-level tasks such as region captioning and visual grounding. Such capabilities are built upon a localized visual tokenization mechanism, where an image input is decomposed into regions of interest and subsequently encoded into region tokens. By integrating region tokens into user instructions and model responses, we seamlessly enable Groma to understand user-specified region inputs and ground its textual output to images. Besides, to enhance the grounded chat ability of Groma, we curate a visually grounded instruction dataset by leveraging the powerful GPT-4V and visual prompting techniques. Compared with MLLMs that rely on the language model or external module for localization, Groma consistently demonstrates superior performances in standard referring and grounding benchmarks, highlighting the advantages of embedding localization into image tokenization. Project page: https://groma-mllm.github.io/. |
2024-04-23T00:00:00 | 2404.13358 | Music Consistency Models | [
"Zhengcong Fei",
"Mingyuan Fan",
"Junshi Huang"
]
| Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps. It has proven to be advantageous in mitigating the computational burdens associated with diffusion models. Nevertheless, the application of consistency models in music generation remains largely unexplored. To address this gap, we present Music Consistency Models (MusicCM), which leverages the concept of consistency models to efficiently synthesize mel-spectrogram for music clips, maintaining high quality while minimizing the number of sampling steps. Building upon existing text-to-music diffusion models, the MusicCM model incorporates consistency distillation and adversarial discriminator training. Moreover, we find it beneficial to generate extended coherent music by incorporating multiple diffusion processes with shared constraints. Experimental results reveal the effectiveness of our model in terms of computational efficiency, fidelity, and naturalness. Notable, MusicCM achieves seamless music synthesis with a mere four sampling steps, e.g., only one second per minute of the music clip, showcasing the potential for real-time application. |
|
2024-04-23T00:00:00 | 2404.14219 | Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone | [
"Marah Abdin",
"Sam Ade Jacobs",
"Ammar Ahmad Awan",
"Jyoti Aneja",
"Ahmed Awadallah",
"Hany Awadalla",
"Nguyen Bach",
"Amit Bahree",
"Arash Bakhtiari",
"Harkirat Behl",
"Alon Benhaim",
"Misha Bilenko",
"Johan Bjorck",
"Sébastien Bubeck",
"Martin Cai",
"Caio César Teodoro Mendes",
"Weizhu Chen",
"Vishrav Chaudhary",
"Parul Chopra",
"Allie Del Giorno",
"Gustavo de Rosa",
"Matthew Dixon",
"Ronen Eldan",
"Dan Iter",
"Abhishek Goswami",
"Suriya Gunasekar",
"Emman Haider",
"Junheng Hao",
"Russell J. Hewett",
"Jamie Huynh",
"Mojan Javaheripi",
"Xin Jin",
"Piero Kauffmann",
"Nikos Karampatziakis",
"Dongwoo Kim",
"Mahoud Khademi",
"Lev Kurilenko",
"James R. Lee",
"Yin Tat Lee",
"Yuanzhi Li",
"Chen Liang",
"Weishung Liu",
"Eric Lin",
"Zeqi Lin",
"Piyush Madan",
"Arindam Mitra",
"Hardik Modi",
"Anh Nguyen",
"Brandon Norick",
"Barun Patra",
"Daniel Perez-Becker",
"Thomas Portet",
"Reid Pryzant",
"Heyang Qin",
"Marko Radmilac",
"Corby Rosset",
"Sambudha Roy",
"Olli Saarikivi",
"Amin Saied",
"Adil Salim",
"Michael Santacroce",
"Shital Shah",
"Ning Shang",
"Hiteshi Sharma",
"Xia Song",
"Olatunji Ruwase",
"Xin Wang",
"Rachel Ward",
"Guanhua Wang",
"Philipp Witte",
"Michael Wyatt",
"Can Xu",
"Jiahang Xu",
"Sonali Yadav",
"Fan Yang",
"Ziyi Yang",
"Donghan Yu",
"Chengruidong Zhang",
"Cyril Zhang",
"Jianwen Zhang",
"Li Lyna Zhang",
"Yi Zhang",
"Yunan Zhang",
"Xiren Zhou"
]
| We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench). |
|
2024-04-23T00:00:00 | 2404.14239 | MultiBooth: Towards Generating All Your Concepts in an Image from Text | [
"Chenyang Zhu",
"Kai Li",
"Yue Ma",
"Chunming He",
"Li Xiu"
]
| https://github.com/chenyangzhu1/MultiBooth | This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text. Despite the significant advancements in customized generation methods, particularly with the success of diffusion models, existing methods often struggle with multi-concept scenarios due to low concept fidelity and high inference cost. MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase. During the single-concept learning phase, we employ a multi-modal image encoder and an efficient concept encoding technique to learn a concise and discriminative representation for each concept. In the multi-concept integration phase, we use bounding boxes to define the generation area for each concept within the cross-attention map. This method enables the creation of individual concepts within their specified regions, thereby facilitating the formation of multi-concept images. This strategy not only improves concept fidelity but also reduces additional inference cost. MultiBooth surpasses various baselines in both qualitative and quantitative evaluations, showcasing its superior performance and computational efficiency. Project Page: https://multibooth.github.io/ |
2024-04-23T00:00:00 | 2404.14394 | A Multimodal Automated Interpretability Agent | [
"Tamar Rott Shaham",
"Sarah Schwettmann",
"Franklin Wang",
"Achyuta Rajaram",
"Evan Hernandez",
"Jacob Andreas",
"Antonio Torralba"
]
| https://github.com/multimodal-interpretability/maia | This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. It equips a pre-trained vision-language model with a set of tools that support iterative experimentation on subcomponents of other models to explain their behavior. These include tools commonly used by human interpretability researchers: for synthesizing and editing inputs, computing maximally activating exemplars from real-world datasets, and summarizing and describing experimental results. Interpretability experiments proposed by MAIA compose these tools to describe and explain system behavior. We evaluate applications of MAIA to computer vision models. We first characterize MAIA's ability to describe (neuron-level) features in learned representations of images. Across several trained models and a novel dataset of synthetic vision neurons with paired ground-truth descriptions, MAIA produces descriptions comparable to those generated by expert human experimenters. We then show that MAIA can aid in two additional interpretability tasks: reducing sensitivity to spurious features, and automatically identifying inputs likely to be mis-classified. |
2024-04-23T00:00:00 | 2404.13686 | Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis | [
"Yuxi Ren",
"Xin Xia",
"Yanzuo Lu",
"Jiacheng Zhang",
"Jie Wu",
"Pan Xie",
"Xing Wang",
"Xuefeng Xiao"
]
| Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs). Current distillation techniques often dichotomize into two distinct aspects: i) ODE Trajectory Preservation; and ii) ODE Trajectory Reformulation. However, these approaches suffer from severe performance degradation or domain shifts. To address these limitations, we propose Hyper-SD, a novel framework that synergistically amalgamates the advantages of ODE Trajectory Preservation and Reformulation, while maintaining near-lossless performance during step compression. Firstly, we introduce Trajectory Segmented Consistency Distillation to progressively perform consistent distillation within pre-defined time-step segments, which facilitates the preservation of the original ODE trajectory from a higher-order perspective. Secondly, we incorporate human feedback learning to boost the performance of the model in a low-step regime and mitigate the performance loss incurred by the distillation process. Thirdly, we integrate score distillation to further improve the low-step generation capability of the model and offer the first attempt to leverage a unified LoRA to support the inference process at all steps. Extensive experiments and user studies demonstrate that Hyper-SD achieves SOTA performance from 1 to 8 inference steps for both SDXL and SD1.5. For example, Hyper-SDXL surpasses SDXL-Lightning by +0.68 in CLIP Score and +0.51 in Aes Score in the 1-step inference. |
|
2024-04-23T00:00:00 | 2404.14396 | SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation | [
"Yuying Ge",
"Sijie Zhao",
"Jinguo Zhu",
"Yixiao Ge",
"Kun Yi",
"Lin Song",
"Chen Li",
"Xiaohan Ding",
"Ying Shan"
]
| https://github.com/AILab-CVC/SEED-X | The rapid evolution of multimodal foundation model has demonstrated significant progresses in vision-language understanding and generation, e.g., our previous work SEED-LLaMA. However, there remains a gap between its capability and the real-world applicability, primarily due to the model's limited capacity to effectively respond to various user instructions and interact with diverse visual data. In this work, we focus on bridging this gap through integrating two enhanced features: (1) comprehending images of arbitrary sizes and ratios, and (2) enabling multi-granularity image generation. We present a unified and versatile foundation model, namely, SEED-X, which is able to model multi-granularity visual semantics for comprehension and generation tasks. Besides the competitive results on public benchmarks, SEED-X demonstrates its effectiveness in handling real-world applications across various domains after instruction tuning. We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications. The models, codes, and datasets will be released in https://github.com/AILab-CVC/SEED-X. |
2024-04-23T00:00:00 | 2404.13050 | FlowMind: Automatic Workflow Generation with LLMs | [
"Zhen Zeng",
"William Watson",
"Nicole Cho",
"Saba Rahimi",
"Shayleen Reynolds",
"Tucker Balch",
"Manuela Veloso"
]
| The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or unpredictable tasks demanded by users. This paper introduces a novel approach, FlowMind, leveraging the capabilities of Large Language Models (LLMs) such as Generative Pretrained Transformer (GPT), to address this limitation and create an automatic workflow generation system. In FlowMind, we propose a generic prompt recipe for a lecture that helps ground LLM reasoning with reliable Application Programming Interfaces (APIs). With this, FlowMind not only mitigates the common issue of hallucinations in LLMs, but also eliminates direct interaction between LLMs and proprietary data or code, thus ensuring the integrity and confidentiality of information - a cornerstone in financial services. FlowMind further simplifies user interaction by presenting high-level descriptions of auto-generated workflows, enabling users to inspect and provide feedback effectively. We also introduce NCEN-QA, a new dataset in finance for benchmarking question-answering tasks from N-CEN reports on funds. We used NCEN-QA to evaluate the performance of workflows generated by FlowMind against baseline and ablation variants of FlowMind. We demonstrate the success of FlowMind, the importance of each component in the proposed lecture recipe, and the effectiveness of user interaction and feedback in FlowMind. |
|
2024-04-23T00:00:00 | 2404.13208 | The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions | [
"Eric Wallace",
"Kai Xiao",
"Reimar Leike",
"Lilian Weng",
"Johannes Heidecke",
"Alex Beutel"
]
| Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts. In this work, we argue that one of the primary vulnerabilities underlying these attacks is that LLMs often consider system prompts (e.g., text from an application developer) to be the same priority as text from untrusted users and third parties. To address this, we propose an instruction hierarchy that explicitly defines how models should behave when instructions of different priorities conflict. We then propose a data generation method to demonstrate this hierarchical instruction following behavior, which teaches LLMs to selectively ignore lower-privileged instructions. We apply this method to GPT-3.5, showing that it drastically increases robustness -- even for attack types not seen during training -- while imposing minimal degradations on standard capabilities. |
|
2024-04-23T00:00:00 | 2404.14047 | How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study | [
"Wei Huang",
"Xudong Ma",
"Haotong Qin",
"Xingyu Zheng",
"Chengtao Lv",
"Hong Chen",
"Jie Luo",
"Xiaojuan Qi",
"Xianglong Liu",
"Michele Magno"
]
| https://github.com/Macaronlin/LLaMA3-Quantization | Meta's LLaMA family has become one of the most powerful open-source Large Language Model (LLM) series. Notably, LLaMA3 models have recently been released and achieve impressive performance across various with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-limited scenarios, we explore LLaMA3's capabilities when quantized to low bit-width. This exploration holds the potential to unveil new insights and challenges for low-bit quantization of LLaMA3 and other forthcoming LLMs, especially in addressing performance degradation problems that suffer in LLM compression. Specifically, we evaluate the 10 existing post-training quantization and LoRA-finetuning methods of LLaMA3 on 1-8 bits and diverse datasets to comprehensively reveal LLaMA3's low-bit quantization performance. Our experiment results indicate that LLaMA3 still suffers non-negligent degradation in these scenarios, especially in ultra-low bit-width. This highlights the significant performance gap under low bit-width that needs to be bridged in future developments. We expect that this empirical study will prove valuable in advancing future models, pushing the LLMs to lower bit-width with higher accuracy for being practical. Our project is released on https://github.com/Macaronlin/LLaMA3-Quantization and quantized LLaMA3 models are released in https://huggingface.co/LLMQ. |
2024-04-23T00:00:00 | 2404.14405 | Learning H-Infinity Locomotion Control | [
"Junfeng Long",
"Wenye Yu",
"Quanyi Li",
"Zirui Wang",
"Dahua Lin",
"Jiangmiao Pang"
]
| https://github.com/OpenRobotLab/HIMLoco | Stable locomotion in precipitous environments is an essential capability of quadruped robots, demanding the ability to resist various external disturbances. However, recent learning-based policies only use basic domain randomization to improve the robustness of learned policies, which cannot guarantee that the robot has adequate disturbance resistance capabilities. In this paper, we propose to model the learning process as an adversarial interaction between the actor and a newly introduced disturber and ensure their optimization with H_{infty} constraint. In contrast to the actor that maximizes the discounted overall reward, the disturber is responsible for generating effective external forces and is optimized by maximizing the error between the task reward and its oracle, i.e., "cost" in each iteration. To keep joint optimization between the actor and the disturber stable, our H_{infty} constraint mandates the bound of ratio between the cost to the intensity of the external forces. Through reciprocal interaction throughout the training phase, the actor can acquire the capability to navigate increasingly complex physical disturbances. We verify the robustness of our approach on quadrupedal locomotion tasks with Unitree Aliengo robot, and also a more challenging task with Unitree A1 robot, where the quadruped is expected to perform locomotion merely on its hind legs as if it is a bipedal robot. The simulated quantitative results show improvement against baselines, demonstrating the effectiveness of the method and each design choice. On the other hand, real-robot experiments qualitatively exhibit how robust the policy is when interfering with various disturbances on various terrains, including stairs, high platforms, slopes, and slippery terrains. All code, checkpoints, and real-world deployment guidance will be made public. |
2024-04-23T00:00:00 | 2404.14351 | Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer | [
"Eric Brachmann",
"Jamie Wynn",
"Shuai Chen",
"Tommaso Cavallari",
"Áron Monszpart",
"Daniyar Turmukhambetov",
"Victor Adrian Prisacariu"
]
| https://github.com/nianticlabs/acezero | We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental reconstruction: they repeat triangulation of sparse 3D points and registration of more camera views to the sparse point cloud. We re-interpret incremental structure-from-motion as an iterated application and refinement of a visual relocalizer, that is, of a method that registers new views to the current state of the reconstruction. This perspective allows us to investigate alternative visual relocalizers that are not rooted in local feature matching. We show that scene coordinate regression, a learning-based relocalization approach, allows us to build implicit, neural scene representations from unposed images. Different from other learning-based reconstruction methods, we do not require pose priors nor sequential inputs, and we optimize efficiently over thousands of images. Our method, ACE0 (ACE Zero), estimates camera poses to an accuracy comparable to feature-based SfM, as demonstrated by novel view synthesis. Project page: https://nianticlabs.github.io/acezero/ |
2024-04-24T00:00:00 | 2404.14619 | OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework | [
"Sachin Mehta",
"Mohammad Hossein Sekhavat",
"Qingqing Cao",
"Maxwell Horton",
"Yanzi Jin",
"Chenfan Sun",
"Iman Mirzadeh",
"Mahyar Najibi",
"Dmitry Belenko",
"Peter Zatloukal",
"Mohammad Rastegari"
]
| https://github.com/apple/corenet | The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks. To this end, we release OpenELM, a state-of-the-art open language model. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. For example, with a parameter budget of approximately one billion parameters, OpenELM exhibits a 2.36% improvement in accuracy compared to OLMo while requiring 2times fewer pre-training tokens. Diverging from prior practices that only provide model weights and inference code, and pre-train on private datasets, our release includes the complete framework for training and evaluation of the language model on publicly available datasets, including training logs, multiple checkpoints, and pre-training configurations. We also release code to convert models to MLX library for inference and fine-tuning on Apple devices. This comprehensive release aims to empower and strengthen the open research community, paving the way for future open research endeavors. Our source code along with pre-trained model weights and training recipes is available at https://github.com/apple/corenet. Additionally, \model models can be found on HuggingFace at: https://huggingface.co/apple/OpenELM. |
2024-04-24T00:00:00 | 2404.14507 | Align Your Steps: Optimizing Sampling Schedules in Diffusion Models | [
"Amirmojtaba Sabour",
"Sanja Fidler",
"Karsten Kreis"
]
| Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sampling speed, relying on many sequential function evaluations through large neural networks. Sampling from DMs can be seen as solving a differential equation through a discretized set of noise levels known as the sampling schedule. While past works primarily focused on deriving efficient solvers, little attention has been given to finding optimal sampling schedules, and the entire literature relies on hand-crafted heuristics. In this work, for the first time, we propose a general and principled approach to optimizing the sampling schedules of DMs for high-quality outputs, called Align Your Steps. We leverage methods from stochastic calculus and find optimal schedules specific to different solvers, trained DMs and datasets. We evaluate our novel approach on several image, video as well as 2D toy data synthesis benchmarks, using a variety of different samplers, and observe that our optimized schedules outperform previous hand-crafted schedules in almost all experiments. Our method demonstrates the untapped potential of sampling schedule optimization, especially in the few-step synthesis regime. |
|
2024-04-24T00:00:00 | 2404.14469 | SnapKV: LLM Knows What You are Looking for Before Generation | [
"Yuhong Li",
"Yingbing Huang",
"Bowen Yang",
"Bharat Venkitesh",
"Acyr Locatelli",
"Hanchen Ye",
"Tianle Cai",
"Patrick Lewis",
"Deming Chen"
]
| https://github.com/FasterDecoding/SnapKV | Large Language Models (LLMs) have made remarkable progress in processing extensive contexts, with the Key-Value (KV) cache playing a vital role in enhancing their performance. However, the growth of the KV cache in response to increasing input length poses challenges to memory and time efficiency. To address this problem, this paper introduces SnapKV, an innovative and fine-tuning-free approach that efficiently minimizes KV cache size while still delivering comparable performance in real-world applications. We discover that each attention head in the model consistently focuses on specific prompt attention features during generation. Meanwhile, this robust pattern can be obtained from an `observation' window located at the end of the prompts. Drawing on this insight, SnapKV automatically compresses KV caches by selecting clustered important KV positions for each attention head. Our approach significantly reduces the growing computational overhead and memory footprint when processing long input sequences. Specifically, SnapKV achieves a consistent decoding speed with a 3.6x increase in generation speed and an 8.2x enhancement in memory efficiency compared to baseline when processing inputs of 16K tokens. At the same time, it maintains comparable performance to baseline models across 16 long sequence datasets. Moreover, SnapKV can process up to 380K context tokens on a single A100-80GB GPU using HuggingFace implementation with minor changes, exhibiting only a negligible accuracy drop in the Needle-in-a-Haystack test. Further comprehensive studies suggest SnapKV's potential for practical applications. |
2024-04-24T00:00:00 | 2404.14700 | FlashSpeech: Efficient Zero-Shot Speech Synthesis | [
"Zhen Ye",
"Zeqian Ju",
"Haohe Liu",
"Xu Tan",
"Jianyi Chen",
"Yiwen Lu",
"Peiwen Sun",
"Jiahao Pan",
"Weizhen Bian",
"Shulin He",
"Qifeng Liu",
"Yike Guo",
"Wei Xue"
]
| Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using a lower computing budget to achieve quality on par with previous work remains a significant challenge. In this paper, we present FlashSpeech, a large-scale zero-shot speech synthesis system with approximately 5\% of the inference time compared with previous work. FlashSpeech is built on the latent consistency model and applies a novel adversarial consistency training approach that can train from scratch without the need for a pre-trained diffusion model as the teacher. Furthermore, a new prosody generator module enhances the diversity of prosody, making the rhythm of the speech sound more natural. The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation. Our experimental results demonstrate the superior performance of FlashSpeech. Notably, FlashSpeech can be about 20 times faster than other zero-shot speech synthesis systems while maintaining comparable performance in terms of voice quality and similarity. Furthermore, FlashSpeech demonstrates its versatility by efficiently performing tasks like voice conversion, speech editing, and diverse speech sampling. Audio samples can be found in https://flashspeech.github.io/. |
|
2024-04-24T00:00:00 | 2404.14687 | Pegasus-v1 Technical Report | [
"Raehyuk Jung",
"Hyojun Go",
"Jaehyuk Yi",
"Jiho Jang",
"Daniel Kim",
"Jay Suh",
"Aiden Lee",
"Cooper Han",
"Jae Lee",
"Jeff Kim",
"Jin-Young Kim",
"Junwan Kim",
"Kyle Park",
"Lucas Lee",
"Mars Ha",
"Minjoon Seo",
"Abraham Jo",
"Ed Park",
"Hassan Kianinejad",
"SJ Kim",
"Tony Moon",
"Wade Jeong",
"Andrei Popescu",
"Esther Kim",
"EK Yoon",
"Genie Heo",
"Henry Choi",
"Jenna Kang",
"Kevin Han",
"Noah Seo",
"Sunny Nguyen",
"Ryan Won",
"Yeonhoo Park",
"Anthony Giuliani",
"Dave Chung",
"Hans Yoon",
"James Le",
"Jenny Ahn",
"June Lee",
"Maninder Saini",
"Meredith Sanders",
"Soyoung Lee",
"Sue Kim",
"Travis Couture"
]
| This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This technical report overviews Pegasus-1's architecture, training strategies, and its performance in benchmarks on video conversation, zero-shot video question answering, and video summarization. We also explore qualitative characteristics of Pegasus-1 , demonstrating its capabilities as well as its limitations, in order to provide readers a balanced view of its current state and its future direction. |
|
2024-04-24T00:00:00 | 2404.15045 | Multi-Head Mixture-of-Experts | [
"Xun Wu",
"Shaohan Huang",
"Wenhui Wang",
"Furu Wei"
]
| https://github.com/yushuiwx/MH-MoE | Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activation, where only a small subset of experts are activated for optimization. (2) Lacking fine-grained analytical capabilities for multiple semantic concepts within individual tokens. We propose Multi-Head Mixture-of-Experts (MH-MoE), which employs a multi-head mechanism to split each token into multiple sub-tokens. These sub-tokens are then assigned to and processed by a diverse set of experts in parallel, and seamlessly reintegrated into the original token form. The multi-head mechanism enables the model to collectively attend to information from various representation spaces within different experts, while significantly enhances expert activation, thus deepens context understanding and alleviate overfitting. Moreover, our MH-MoE is straightforward to implement and decouples from other SMoE optimization methods, making it easy to integrate with other SMoE models for enhanced performance. Extensive experimental results across three tasks: English-focused language modeling, Multi-lingual language modeling and Masked multi-modality modeling tasks, demonstrate the effectiveness of MH-MoE. |
2024-04-24T00:00:00 | 2404.14994 | Transformers Can Represent n-gram Language Models | [
"Anej Svete",
"Ryan Cotterell"
]
| https://github.com/rycolab/transformer-ngrams | Plenty of existing work has analyzed the abilities of the transformer architecture by describing its representational capacity with formal models of computation. However, the focus so far has been on analyzing the architecture in terms of language acceptance. We contend that this is an ill-suited problem in the study of language models (LMs), which are definitionally probability distributions over strings. In this paper, we focus on the relationship between transformer LMs and n-gram LMs, a simple and historically relevant class of language models. We show that transformer LMs using the hard or sparse attention mechanisms can exactly represent any n-gram LM, giving us a concrete lower bound on their probabilistic representational capacity. This provides a first step towards understanding the mechanisms that transformer LMs can use to represent probability distributions over strings. |
2024-04-25T00:00:00 | 2404.16022 | PuLID: Pure and Lightning ID Customization via Contrastive Alignment | [
"Zinan Guo",
"Yanze Wu",
"Zhuowei Chen",
"Lang Chen",
"Qian He"
]
| https://github.com/ToTheBeginning/PuLID | We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation. By incorporating a Lightning T2I branch with a standard diffusion one, PuLID introduces both contrastive alignment loss and accurate ID loss, minimizing disruption to the original model and ensuring high ID fidelity. Experiments show that PuLID achieves superior performance in both ID fidelity and editability. Another attractive property of PuLID is that the image elements (e.g., background, lighting, composition, and style) before and after the ID insertion are kept as consistent as possible. Codes and models will be available at https://github.com/ToTheBeginning/PuLID |
2024-04-25T00:00:00 | 2404.15449 | ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning | [
"Weifeng Chen",
"Jiacheng Zhang",
"Jie Wu",
"Hefeng Wu",
"Xuefeng Xiao",
"Liang Lin"
]
| https://github.com/Weifeng-Chen/ID-Aligner | The rapid development of diffusion models has triggered diverse applications. Identity-preserving text-to-image generation (ID-T2I) particularly has received significant attention due to its wide range of application scenarios like AI portrait and advertising. While existing ID-T2I methods have demonstrated impressive results, several key challenges remain: (1) It is hard to maintain the identity characteristics of reference portraits accurately, (2) The generated images lack aesthetic appeal especially while enforcing identity retention, and (3) There is a limitation that cannot be compatible with LoRA-based and Adapter-based methods simultaneously. To address these issues, we present ID-Aligner, a general feedback learning framework to enhance ID-T2I performance. To resolve identity features lost, we introduce identity consistency reward fine-tuning to utilize the feedback from face detection and recognition models to improve generated identity preservation. Furthermore, we propose identity aesthetic reward fine-tuning leveraging rewards from human-annotated preference data and automatically constructed feedback on character structure generation to provide aesthetic tuning signals. Thanks to its universal feedback fine-tuning framework, our method can be readily applied to both LoRA and Adapter models, achieving consistent performance gains. Extensive experiments on SD1.5 and SDXL diffusion models validate the effectiveness of our approach. Project Page: \url{https://idaligner.github.io/} |
2024-04-25T00:00:00 | 2404.15789 | MotionMaster: Training-free Camera Motion Transfer For Video Generation | [
"Teng Hu",
"Jiangning Zhang",
"Ran Yi",
"Yating Wang",
"Hongrui Huang",
"Jieyu Weng",
"Yabiao Wang",
"Lizhuang Ma"
]
| https://github.com/sjtuplayer/MotionMaster | The emergence of diffusion models has greatly propelled the progress in image and video generation. Recently, some efforts have been made in controllable video generation, including text-to-video generation and video motion control, among which camera motion control is an important topic. However, existing camera motion control methods rely on training a temporal camera module, and necessitate substantial computation resources due to the large amount of parameters in video generation models. Moreover, existing methods pre-define camera motion types during training, which limits their flexibility in camera control. Therefore, to reduce training costs and achieve flexible camera control, we propose COMD, a novel training-free video motion transfer model, which disentangles camera motions and object motions in source videos and transfers the extracted camera motions to new videos. We first propose a one-shot camera motion disentanglement method to extract camera motion from a single source video, which separates the moving objects from the background and estimates the camera motion in the moving objects region based on the motion in the background by solving a Poisson equation. Furthermore, we propose a few-shot camera motion disentanglement method to extract the common camera motion from multiple videos with similar camera motions, which employs a window-based clustering technique to extract the common features in temporal attention maps of multiple videos. Finally, we propose a motion combination method to combine different types of camera motions together, enabling our model a more controllable and flexible camera control. Extensive experiments demonstrate that our training-free approach can effectively decouple camera-object motion and apply the decoupled camera motion to a wide range of controllable video generation tasks, achieving flexible and diverse camera motion control. |
2024-04-25T00:00:00 | 2404.16029 | Editable Image Elements for Controllable Synthesis | [
"Jiteng Mu",
"Michaël Gharbi",
"Richard Zhang",
"Eli Shechtman",
"Nuno Vasconcelos",
"Xiaolong Wang",
"Taesung Park"
]
| Diffusion models have made significant advances in text-guided synthesis tasks. However, editing user-provided images remains challenging, as the high dimensional noise input space of diffusion models is not naturally suited for image inversion or spatial editing. In this work, we propose an image representation that promotes spatial editing of input images using a diffusion model. Concretely, we learn to encode an input into "image elements" that can faithfully reconstruct an input image. These elements can be intuitively edited by a user, and are decoded by a diffusion model into realistic images. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition. Project page: https://jitengmu.github.io/Editable_Image_Elements/ |
|
2024-04-25T00:00:00 | 2404.15420 | XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference | [
"João Monteiro",
"Étienne Marcotte",
"Pierre-André Noël",
"Valentina Zantedeschi",
"David Vázquez",
"Nicolas Chapados",
"Christopher Pal",
"Perouz Taslakian"
]
| In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference information. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable. However, caching transformer states can easily require almost as much space as the model parameters. When the right context isn't known in advance, caching ICL can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. More precisely, we leverage pre-trained decoder-only models and only train a small number of added layers. We use Question-Answering (QA) as a testbed to evaluate the ability of our models to perform conditional generation and observe that they outperform ICL, are comparable to fine-tuned prompted LLMs, and drastically reduce the space footprint relative to standard KV caching by two orders of magnitude. |
|
2024-04-25T00:00:00 | 2404.15778 | BASS: Batched Attention-optimized Speculative Sampling | [
"Haifeng Qian",
"Sujan Kumar Gonugondla",
"Sungsoo Ha",
"Mingyue Shang",
"Sanjay Krishna Gouda",
"Ramesh Nallapati",
"Sudipta Sengupta",
"Xiaofei Ma",
"Anoop Deoras"
]
| Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. However, most existing implementations focus on generating a single sequence. Real-world generative AI applications often require multiple responses and how to perform speculative decoding in a batched setting while preserving its latency benefits poses non-trivial challenges. This paper describes a system of batched speculative decoding that sets a new state of the art in multi-sequence generation latency and that demonstrates superior GPU utilization as well as quality of generations within a time budget. For example, for a 7.8B-size model on a single A100 GPU and with a batch size of 8, each sequence is generated at an average speed of 5.8ms per token, the overall throughput being 1.1K tokens per second. These results represent state-of-the-art latency and a 2.15X speed-up over optimized regular decoding. Within a time budget that regular decoding does not finish, our system is able to generate sequences with HumanEval Pass@First of 43% and Pass@All of 61%, far exceeding what's feasible with single-sequence speculative decoding. Our peak GPU utilization during decoding reaches as high as 15.8%, more than 3X the highest of that of regular decoding and around 10X of single-sequence speculative decoding. |
|
2024-04-25T00:00:00 | 2404.16030 | MoDE: CLIP Data Experts via Clustering | [
"Jiawei Ma",
"Po-Yao Huang",
"Saining Xie",
"Shang-Wen Li",
"Luke Zettlemoyer",
"Shih-Fu Chang",
"Wen-Tau Yih",
"Hu Xu"
]
| https://github.com/facebookresearch/MetaCLIP/tree/main/mode | The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster, being less sensitive to false negative noises in other clusters. At inference time, we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely, the samples in one cluster should be semantically similar, but the number of data experts should still be reasonable for training and inference. As such, we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less (<35\%) training cost. Meanwhile, MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available at https://github.com/facebookresearch/MetaCLIP/tree/main/mode. |
2024-04-25T00:00:00 | 2404.15653 | CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data | [
"Sachin Mehta",
"Maxwell Horton",
"Fartash Faghri",
"Mohammad Hossein Sekhavat",
"Mahyar Najibi",
"Mehrdad Farajtabar",
"Oncel Tuzel",
"Mohammad Rastegari"
]
| https://github.com/apple/corenet | Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings. However, pairwise similarity computation in contrastive loss between image and text pairs poses computational challenges. This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text data. The proposed method reframes pre-training on image-text data as a classification task. Consequently, it eliminates the need for pairwise similarity computations in contrastive loss, achieving a remarkable 2.7times acceleration in training speed compared to contrastive learning on web-scale data. Through extensive experiments spanning diverse vision tasks, including detection and segmentation, we demonstrate that the proposed method maintains high representation quality. Our source code along with pre-trained model weights and training recipes is available at https://github.com/apple/corenet. |
2024-04-25T00:00:00 | 2404.16035 | MaGGIe: Masked Guided Gradual Human Instance Matting | [
"Chuong Huynh",
"Seoung Wug Oh",
"Abhinav Shrivastava",
"Joon-Young Lee"
]
| https://github.com/hmchuong/MaGGIe | Human matting is a foundation task in image and video processing, where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe, Masked Guided Gradual Human Instance Matting, which predicts alpha mattes progressively for each human instances while maintaining the computational cost, precision, and consistency. Our method leverages modern architectures, including transformer attention and sparse convolution, to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario, our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks, the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. |
2024-04-26T00:00:00 | 2404.16510 | Interactive3D: Create What You Want by Interactive 3D Generation | [
"Shaocong Dong",
"Lihe Ding",
"Zhanpeng Huang",
"Zibin Wang",
"Tianfan Xue",
"Dan Xu"
]
| https://github.com/interactive-3d/interactive3d | 3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do not align with user expectations, thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current generative models due to limited interaction capabilities. Existing methods mainly offer two approaches: (i) interpreting textual instructions with constrained controllability, or (ii) reconstructing 3D objects from 2D images. Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process, restricting the scope for direct and versatile 3D modifications. In this work, we introduce Interactive3D, an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages, utilizing distinct 3D representations. The first stage employs Gaussian Splatting for direct user interaction, allowing modifications and guidance of the generative direction at any intermediate step through (i) Adding and Removing components, (ii) Deformable and Rigid Dragging, (iii) Geometric Transformations, and (iv) Semantic Editing. Subsequently, the Gaussian splats are transformed into InstantNGP. We introduce a novel (v) Interactive Hash Refinement module to further add details and extract the geometry in the second stage. Our experiments demonstrate that Interactive3D markedly improves the controllability and quality of 3D generation. Our project webpage is available at https://interactive-3d.github.io/. |
2024-04-26T00:00:00 | 2404.16790 | SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension | [
"Bohao Li",
"Yuying Ge",
"Yi Chen",
"Yixiao Ge",
"Ruimao Zhang",
"Ying Shan"
]
| https://github.com/AILab-CVC/SEED-Bench | Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real world, which are characterized by the presence of extensive texts embedded within images. Recently, the advent of MLLMs with impressive versatility has raised the bar for what we can expect from MLLMs. However, their proficiency in text-rich scenarios has yet to be comprehensively and objectively assessed, since current MLLM benchmarks primarily focus on evaluating general visual comprehension. In this work, we introduce SEED-Bench-2-Plus, a benchmark specifically designed for evaluating text-rich visual comprehension of MLLMs. Our benchmark comprises 2.3K multiple-choice questions with precise human annotations, spanning three broad categories: Charts, Maps, and Webs, each of which covers a wide spectrum of text-rich scenarios in the real world. These categories, due to their inherent complexity and diversity, effectively simulate real-world text-rich environments. We further conduct a thorough evaluation involving 34 prominent MLLMs (including GPT-4V, Gemini-Pro-Vision and Claude-3-Opus) and emphasize the current limitations of MLLMs in text-rich visual comprehension. We hope that our work can serve as a valuable addition to existing MLLM benchmarks, providing insightful observations and inspiring further research in the area of text-rich visual comprehension with MLLMs. The dataset and evaluation code can be accessed at https://github.com/AILab-CVC/SEED-Bench. |
2024-04-26T00:00:00 | 2404.16821 | How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites | [
"Zhe Chen",
"Weiyun Wang",
"Hao Tian",
"Shenglong Ye",
"Zhangwei Gao",
"Erfei Cui",
"Wenwen Tong",
"Kongzhi Hu",
"Jiapeng Luo",
"Zheng Ma",
"Ji Ma",
"Jiaqi Wang",
"Xiaoyi Dong",
"Hang Yan",
"Hewei Guo",
"Conghui He",
"Zhenjiang Jin",
"Chao Xu",
"Bin Wang",
"Xingjian Wei",
"Wei Li",
"Wenjian Zhang",
"Lewei Lu",
"Xizhou Zhu",
"Tong Lu",
"Dahua Lin",
"Yu Qiao"
]
| https://github.com/OpenGVLab/InternVL | In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements: (1) Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model -- InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. (2) Dynamic High-Resolution: we divide images into tiles ranging from 1 to 40 of 448times448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input. (3) High-Quality Bilingual Dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks. Code has been released at https://github.com/OpenGVLab/InternVL. |
2024-04-26T00:00:00 | 2404.16645 | Tele-FLM Technical Report | [
"Xiang Li",
"Yiqun Yao",
"Xin Jiang",
"Xuezhi Fang",
"Chao Wang",
"Xinzhang Liu",
"Zihan Wang",
"Yu Zhao",
"Xin Wang",
"Yuyao Huang",
"Shuangyong Song",
"Yongxiang Li",
"Zheng Zhang",
"Bo Zhao",
"Aixin Sun",
"Yequan Wang",
"Zhongjiang He",
"Zhongyuan Wang",
"Xuelong Li",
"Tiejun Huang"
]
| Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications. However, there is a notable paucity of detailed, open-sourced methodologies on efficiently scaling LLMs beyond 50 billion parameters with minimum trial-and-error cost and computational resources. In this report, we introduce Tele-FLM (aka FLM-2), a 52B open-sourced multilingual large language model that features a stable, efficient pre-training paradigm and enhanced factual judgment capabilities. Tele-FLM demonstrates superior multilingual language modeling abilities, measured by BPB on textual corpus. Besides, in both English and Chinese foundation model evaluation, it is comparable to strong open-sourced models that involve larger pre-training FLOPs, such as Llama2-70B and DeepSeek-67B. In addition to the model weights, we share the core designs, engineering practices, and training details, which we expect to benefit both the academic and industrial communities. |
|
2024-04-26T00:00:00 | 2404.16710 | Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding | [
"Mostafa Elhoushi",
"Akshat Shrivastava",
"Diana Liskovich",
"Basil Hosmer",
"Bram Wasti",
"Liangzhen Lai",
"Anas Mahmoud",
"Bilge Acun",
"Saurabh Agarwal",
"Ahmed Roman",
"Ahmed A Aly",
"Beidi Chen",
"Carole-Jean Wu"
]
| We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task. |
|
2024-04-26T00:00:00 | 2404.16771 | ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving | [
"Jiehui Huang",
"Xiao Dong",
"Wenhui Song",
"Hanhui Li",
"Jun Zhou",
"Yuhao Cheng",
"Shutao Liao",
"Long Chen",
"Yiqiang Yan",
"Shengcai Liao",
"Xiaodan Liang"
]
| https://github.com/JackAILab/ConsistentID | Diffusion-based technologies have made significant strides, particularly in personalized and customized facialgeneration. However, existing methods face challenges in achieving high-fidelity and detailed identity (ID)consistency, primarily due to insufficient fine-grained control over facial areas and the lack of a comprehensive strategy for ID preservation by fully considering intricate facial details and the overall face. To address these limitations, we introduce ConsistentID, an innovative method crafted for diverseidentity-preserving portrait generation under fine-grained multimodal facial prompts, utilizing only a single reference image. ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions. Together, these components significantly enhance the accuracy of ID preservation by introducing fine-grained multimodal ID information from facial regions. To facilitate training of ConsistentID, we present a fine-grained portrait dataset, FGID, with over 500,000 facial images, offering greater diversity and comprehensiveness than existing public facial datasets. % such as LAION-Face, CelebA, FFHQ, and SFHQ. Experimental results substantiate that our ConsistentID achieves exceptional precision and diversity in personalized facial generation, surpassing existing methods in the MyStyle dataset. Furthermore, while ConsistentID introduces more multimodal ID information, it maintains a fast inference speed during generation. |
2024-04-26T00:00:00 | 2404.16811 | Make Your LLM Fully Utilize the Context | [
"Shengnan An",
"Zexiong Ma",
"Zeqi Lin",
"Nanning Zheng",
"Jian-Guang Lou"
]
| https://github.com/microsoft/FILM | While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge. We hypothesize that it stems from insufficient explicit supervision during the long-context training, which fails to emphasize that any position in a long context can hold crucial information. Based on this intuition, our study presents information-intensive (IN2) training, a purely data-driven solution to overcome lost-in-the-middle. Specifically, IN2 training leverages a synthesized long-context question-answer dataset, where the answer requires (1) fine-grained information awareness on a short segment (~128 tokens) within a synthesized long context (4K-32K tokens), and (2) the integration and reasoning of information from two or more short segments. Through applying this information-intensive training on Mistral-7B, we present FILM-7B (FILl-in-the-Middle). To thoroughly assess the ability of FILM-7B for utilizing long contexts, we design three probing tasks that encompass various context styles (document, code, and structured-data context) and information retrieval patterns (forward, backward, and bi-directional retrieval). The probing results demonstrate that FILM-7B can robustly retrieve information from different positions in its 32K context window. Beyond these probing tasks, FILM-7B significantly improves the performance on real-world long-context tasks (e.g., 23.5->26.9 F1 score on NarrativeQA), while maintaining a comparable performance on short-context tasks (e.g., 59.3->59.2 accuracy on MMLU). Github Link: https://github.com/microsoft/FILM. |
2024-04-26T00:00:00 | 2404.16221 | NeRF-XL: Scaling NeRFs with Multiple GPUs | [
"Ruilong Li",
"Sanja Fidler",
"Angjoo Kanazawa",
"Francis Williams"
]
| We present NeRF-XL, a principled method for distributing Neural Radiance Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering of NeRFs with an arbitrarily large capacity. We begin by revisiting existing multi-GPU approaches, which decompose large scenes into multiple independently trained NeRFs, and identify several fundamental issues with these methods that hinder improvements in reconstruction quality as additional computational resources (GPUs) are used in training. NeRF-XL remedies these issues and enables the training and rendering of NeRFs with an arbitrary number of parameters by simply using more hardware. At the core of our method lies a novel distributed training and rendering formulation, which is mathematically equivalent to the classic single-GPU case and minimizes communication between GPUs. By unlocking NeRFs with arbitrarily large parameter counts, our approach is the first to reveal multi-GPU scaling laws for NeRFs, showing improvements in reconstruction quality with larger parameter counts and speed improvements with more GPUs. We demonstrate the effectiveness of NeRF-XL on a wide variety of datasets, including the largest open-source dataset to date, MatrixCity, containing 258K images covering a 25km^2 city area. |
|
2024-04-26T00:00:00 | 2404.16820 | Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings | [
"Olivia Wiles",
"Chuhan Zhang",
"Isabela Albuquerque",
"Ivana Kajić",
"Su Wang",
"Emanuele Bugliarello",
"Yasumasa Onoe",
"Chris Knutsen",
"Cyrus Rashtchian",
"Jordi Pont-Tuset",
"Aida Nematzadeh"
]
| https://github.com/google-deepmind/gecko_benchmark_t2i | While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt. While previous work has evaluated T2I alignment by proposing metrics, benchmarks, and templates for collecting human judgements, the quality of these components is not systematically measured. Human-rated prompt sets are generally small and the reliability of the ratings -- and thereby the prompt set used to compare models -- is not evaluated. We address this gap by performing an extensive study evaluating auto-eval metrics and human templates. We provide three main contributions: (1) We introduce a comprehensive skills-based benchmark that can discriminate models across different human templates. This skills-based benchmark categorises prompts into sub-skills, allowing a practitioner to pinpoint not only which skills are challenging, but at what level of complexity a skill becomes challenging. (2) We gather human ratings across four templates and four T2I models for a total of >100K annotations. This allows us to understand where differences arise due to inherent ambiguity in the prompt and where they arise due to differences in metric and model quality. (3) Finally, we introduce a new QA-based auto-eval metric that is better correlated with human ratings than existing metrics for our new dataset, across different human templates, and on TIFA160. |
2024-04-26T00:00:00 | 2404.16375 | List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs | [
"An Yan",
"Zhengyuan Yang",
"Junda Wu",
"Wanrong Zhu",
"Jianwei Yang",
"Linjie Li",
"Kevin Lin",
"Jianfeng Wang",
"Julian McAuley",
"Jianfeng Gao",
"Lijuan Wang"
]
| https://github.com/zzxslp/SoM-LLaVA | Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image. These tags, marked with alphanumerics, can be indexed via text tokens for easy reference. Despite the extraordinary performance from GPT-4V, we observe that other Multimodal Large Language Models (MLLMs) struggle to understand these visual tags. To promote the learning of SoM prompting for open-source models, we propose a new learning paradigm: "list items one by one," which asks the model to enumerate and describe all visual tags placed on the image following the alphanumeric orders of tags. By integrating our curated dataset with other visual instruction tuning datasets, we are able to equip existing MLLMs with the SoM prompting ability. Furthermore, we evaluate our finetuned SoM models on five MLLM benchmarks. We find that this new dataset, even in a relatively small size (10k-30k images with tags), significantly enhances visual reasoning capabilities and reduces hallucinations for MLLMs. Perhaps surprisingly, these improvements persist even when the visual tags are omitted from input images during inference. This suggests the potential of "list items one by one" as a new paradigm for training MLLMs, which strengthens the object-text alignment through the use of visual tags in the training stage. Finally, we conduct analyses by probing trained models to understand the working mechanism of SoM. Our code and data are available at https://github.com/zzxslp/SoM-LLaVA. |
2024-04-29T00:00:00 | 2404.16873 | AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs | [
"Anselm Paulus",
"Arman Zharmagambetov",
"Chuan Guo",
"Brandon Amos",
"Yuandong Tian"
]
| https://github.com/facebookresearch/advprompter | While recently Large Language Models (LLMs) have achieved remarkable successes, they are vulnerable to certain jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires finding adversarial prompts that cause such jailbreaking, e.g. by appending a suffix to a given instruction, which is inefficient and time-consuming. On the other hand, automatic adversarial prompt generation often leads to semantically meaningless attacks that can easily be detected by perplexity-based filters, may require gradient information from the TargetLLM, or do not scale well due to time-consuming discrete optimization processes over the token space. In this paper, we present a novel method that uses another LLM, called the AdvPrompter, to generate human-readable adversarial prompts in seconds, sim800times faster than existing optimization-based approaches. We train the AdvPrompter using a novel algorithm that does not require access to the gradients of the TargetLLM. This process alternates between two steps: (1) generating high-quality target adversarial suffixes by optimizing the AdvPrompter predictions, and (2) low-rank fine-tuning of the AdvPrompter with the generated adversarial suffixes. The trained AdvPrompter generates suffixes that veil the input instruction without changing its meaning, such that the TargetLLM is lured to give a harmful response. Experimental results on popular open source TargetLLMs show state-of-the-art results on the AdvBench dataset, that also transfer to closed-source black-box LLM APIs. Further, we demonstrate that by fine-tuning on a synthetic dataset generated by AdvPrompter, LLMs can be made more robust against jailbreaking attacks while maintaining performance, i.e. high MMLU scores. |
2024-04-29T00:00:00 | 2404.16845 | HaLo-NeRF: Learning Geometry-Guided Semantics for Exploring Unconstrained Photo Collections | [
"Chen Dudai",
"Morris Alper",
"Hana Bezalel",
"Rana Hanocka",
"Itai Lang",
"Hadar Averbuch-Elor"
]
| https://github.com/TAU-VAILab/HaLo-NeRF | Internet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large-scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine-grained understanding. In constrained 3D domains, recent methods have leveraged vision-and-language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain. In this work, we present a localization system that connects neural representations of scenes depicting large-scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision-and-language models with adaptations for understanding landmark scene semantics. To bolster such models with fine-grained knowledge, we leverage large-scale Internet data containing images of similar landmarks along with weakly-related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D-compatible segmentation that ultimately lifts to a volumetric scene representation. Our results show that HaLo-NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our project page is at https://tau-vailab.github.io/HaLo-NeRF/. |
2024-04-29T00:00:00 | 2404.16994 | PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning | [
"Lin Xu",
"Yilin Zhao",
"Daquan Zhou",
"Zhijie Lin",
"See Kiong Ng",
"Jiashi Feng"
]
| https://github.com/magic-research/PLLaVA | Vision-language pre-training has significantly elevated performance across a wide range of image-language applications. Yet, the pre-training process for video-related tasks demands exceptionally large computational and data resources, which hinders the progress of video-language models. This paper investigates a straightforward, highly efficient, and resource-light approach to adapting an existing image-language pre-trained model for dense video understanding. Our preliminary experiments reveal that directly fine-tuning pre-trained image-language models with multiple frames as inputs on video datasets leads to performance saturation or even a drop. Our further investigation reveals that it is largely attributed to the bias of learned high-norm visual features. Motivated by this finding, we propose a simple but effective pooling strategy to smooth the feature distribution along the temporal dimension and thus reduce the dominant impacts from the extreme features. The new model is termed Pooling LLaVA, or in short. achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks. Notably, on the recent popular Video ChatGPT benchmark, PLLaVA achieves a score of 3.48 out of 5 on average of five evaluated dimensions, exceeding the previous SOTA results from GPT4V (IG-VLM) by 9\%. On the latest multi-choice benchmark MVBench, PLLaVA achieves 58.1\% accuracy on average across 20 sub-tasks, 14.5\% higher than GPT4V (IG-VLM). Code is available at https://github.com/magic-research/PLLaVA. |
2024-04-29T00:00:00 | 2404.17569 | MaPa: Text-driven Photorealistic Material Painting for 3D Shapes | [
"Shangzhan Zhang",
"Sida Peng",
"Tao Xu",
"Yuanbo Yang",
"Tianrun Chen",
"Nan Xue",
"Yujun Shen",
"Hujun Bao",
"Ruizhen Hu",
"Xiaowei Zhou"
]
| This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs as the appearance representation, which supports high-quality rendering and provides substantial flexibility in editing. Instead of relying on extensive paired data, i.e., 3D meshes with material graphs and corresponding text descriptions, to train a material graph generative model, we propose to leverage the pre-trained 2D diffusion model as a bridge to connect the text and material graphs. Specifically, our approach decomposes a shape into a set of segments and designs a segment-controlled diffusion model to synthesize 2D images that are aligned with mesh parts. Based on generated images, we initialize parameters of material graphs and fine-tune them through the differentiable rendering module to produce materials in accordance with the textual description. Extensive experiments demonstrate the superior performance of our framework in photorealism, resolution, and editability over existing methods. Project page: https://zhanghe3z.github.io/MaPa/ |
|
2024-04-30T00:00:00 | 2404.17521 | Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations | [
"Puhao Li",
"Tengyu Liu",
"Yuyang Li",
"Muzhi Han",
"Haoran Geng",
"Shu Wang",
"Yixin Zhu",
"Song-Chun Zhu",
"Siyuan Huang"
]
| https://github.com/xiaoyao-li/Ag2Manip | Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, modern methods (e.g., VIP and R3M) still face significant hurdles, notably the domain gap among robotic embodiments and the sparsity of successful task executions within specific action spaces, resulting in misaligned and ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic representations for Manipulation), a framework aimed at surmounting these challenges through two key innovations: a novel agent-agnostic visual representation derived from human manipulation videos, with the specifics of embodiments obscured to enhance generalizability; and an agent-agnostic action representation abstracting a robot's kinematics to a universal agent proxy, emphasizing crucial interactions between end-effector and object. Ag2Manip's empirical validation across simulated benchmarks like FrankaKitchen, ManiSkill, and PartManip shows a 325% increase in performance, achieved without domain-specific demonstrations. Ablation studies underline the essential contributions of the visual and action representations to this success. Extending our evaluations to the real world, Ag2Manip significantly improves imitation learning success rates from 50% to 77.5%, demonstrating its effectiveness and generalizability across both simulated and physical environments. |
2024-04-30T00:00:00 | 2404.18416 | Capabilities of Gemini Models in Medicine | [
"Khaled Saab",
"Tao Tu",
"Wei-Hung Weng",
"Ryutaro Tanno",
"David Stutz",
"Ellery Wulczyn",
"Fan Zhang",
"Tim Strother",
"Chunjong Park",
"Elahe Vedadi",
"Juanma Zambrano Chaves",
"Szu-Yeu Hu",
"Mike Schaekermann",
"Aishwarya Kamath",
"Yong Cheng",
"David G. T. Barrett",
"Cathy Cheung",
"Basil Mustafa",
"Anil Palepu",
"Daniel McDuff",
"Le Hou",
"Tomer Golany",
"Luyang Liu",
"Jean-baptiste Alayrac",
"Neil Houlsby",
"Nenad Tomasev",
"Jan Freyberg",
"Charles Lau",
"Jonas Kemp",
"Jeremy Lai",
"Shekoofeh Azizi",
"Kimberly Kanada",
"SiWai Man",
"Kavita Kulkarni",
"Ruoxi Sun",
"Siamak Shakeri",
"Luheng He",
"Ben Caine",
"Albert Webson",
"Natasha Latysheva",
"Melvin Johnson",
"Philip Mansfield",
"Jian Lu",
"Ehud Rivlin",
"Jesper Anderson",
"Bradley Green",
"Renee Wong",
"Jonathan Krause",
"Jonathon Shlens",
"Ewa Dominowska",
"S. M. Ali Eslami",
"Claire Cui",
"Oriol Vinyals",
"Koray Kavukcuoglu",
"James Manyika",
"Jeff Dean",
"Demis Hassabis",
"Yossi Matias",
"Dale Webster",
"Joelle Barral",
"Greg Corrado",
"Christopher Semturs",
"S. Sara Mahdavi",
"Juraj Gottweis",
"Alan Karthikesalingam",
"Vivek Natarajan"
]
| Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain. |
|
2024-04-30T00:00:00 | 2404.18243 | LEGENT: Open Platform for Embodied Agents | [
"Zhili Cheng",
"Zhitong Wang",
"Jinyi Hu",
"Shengding Hu",
"An Liu",
"Yuge Tu",
"Pengkai Li",
"Lei Shi",
"Zhiyuan Liu",
"Maosong Sun"
]
| Despite advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hindering complex real-life task performance in physical environments. Existing integrations often feature limited open sourcing, challenging collective progress in this field. We introduce LEGENT, an open, scalable platform for developing embodied agents using LLMs and LMMs. LEGENT offers a dual approach: a rich, interactive 3D environment with communicable and actionable agents, paired with a user-friendly interface, and a sophisticated data generation pipeline utilizing advanced algorithms to exploit supervision from simulated worlds at scale. In our experiments, an embryonic vision-language-action model trained on LEGENT-generated data surpasses GPT-4V in embodied tasks, showcasing promising generalization capabilities. |
|
2024-04-30T00:00:00 | 2404.18796 | Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models | [
"Pat Verga",
"Sebastian Hofstatter",
"Sophia Althammer",
"Yixuan Su",
"Aleksandra Piktus",
"Arkady Arkhangorodsky",
"Minjie Xu",
"Naomi White",
"Patrick Lewis"
]
| As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe particular model properties difficult, but evaluating the correctness of a model's freeform generation alone is a challenge. To address this, many evaluations now rely on using LLMs themselves as judges to score the quality of outputs from other LLMs. Evaluations most commonly use a single large model like GPT4. While this method has grown in popularity, it is costly, has been shown to introduce intramodel bias, and in this work, we find that very large models are often unnecessary. We propose instead to evaluate models using a Panel of LLm evaluators (PoLL). Across three distinct judge settings and spanning six different datasets, we find that using a PoLL composed of a larger number of smaller models outperforms a single large judge, exhibits less intra-model bias due to its composition of disjoint model families, and does so while being over seven times less expensive. |
|
2024-04-30T00:00:00 | 2404.17672 | BlenderAlchemy: Editing 3D Graphics with Vision-Language Models | [
"Ian Huang",
"Guandao Yang",
"Leonidas Guibas"
]
| https://github.com/ianhuang0630/BlenderAlchemyOfficial | Graphics design is important for various applications, including movie production and game design. To create a high-quality scene, designers usually need to spend hours in software like Blender, in which they might need to interleave and repeat operations, such as connecting material nodes, hundreds of times. Moreover, slightly different design goals may require completely different sequences, making automation difficult. In this paper, we propose a system that leverages Vision-Language Models (VLMs), like GPT-4V, to intelligently search the design action space to arrive at an answer that can satisfy a user's intent. Specifically, we design a vision-based edit generator and state evaluator to work together to find the correct sequence of actions to achieve the goal. Inspired by the role of visual imagination in the human design process, we supplement the visual reasoning capabilities of VLMs with "imagined" reference images from image-generation models, providing visual grounding of abstract language descriptions. In this paper, we provide empirical evidence suggesting our system can produce simple but tedious Blender editing sequences for tasks such as editing procedural materials from text and/or reference images, as well as adjusting lighting configurations for product renderings in complex scenes. |
2024-04-30T00:00:00 | 2404.18911 | Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting | [
"Fangcheng Liu",
"Yehui Tang",
"Zhenhua Liu",
"Yunsheng Ni",
"Kai Han",
"Yunhe Wang"
]
| https://github.com/Equationliu/Kangaroo | Speculative decoding has demonstrated its effectiveness in accelerating the inference of large language models while maintaining a consistent sampling distribution. However, the conventional approach of training a separate draft model to achieve a satisfactory token acceptance rate can be costly. Drawing inspiration from early exiting, we propose a novel self-speculative decoding framework Kangaroo, which uses a fixed shallow sub-network as a self-draft model, with the remaining layers serving as the larger target model. We train a lightweight and efficient adapter module on top of the sub-network to bridge the gap between the sub-network and the full model's representation ability. It is noteworthy that the inference latency of the self-draft model may no longer be negligible compared to the large model, necessitating strategies to increase the token acceptance rate while minimizing the drafting steps of the small model. To address this challenge, we introduce an additional early exiting mechanism for generating draft tokens. Specifically, we halt the small model's subsequent prediction during the drafting phase once the confidence level for the current token falls below a certain threshold. Extensive experiments on the Spec-Bench demonstrate the effectiveness of Kangaroo. Under single-sequence verification, Kangaroo achieves speedups up to 1.68times on Spec-Bench, outperforming Medusa-1 with 88.7\% fewer additional parameters (67M compared to 591M). The code for Kangaroo is available at https://github.com/Equationliu/Kangaroo. |
2024-04-30T00:00:00 | 2401.16465 | DressCode: Autoregressively Sewing and Generating Garments from Text Guidance | [
"Kai He",
"Kaixin Yao",
"Qixuan Zhang",
"Jingyi Yu",
"Lingjie Liu",
"Lan Xu"
]
| https://github.com/IHe-KaiI/DressCode | Apparel's significant role in human appearance underscores the importance of garment digitalization for digital human creation. Recent advances in 3D content creation are pivotal for digital human creation. Nonetheless, garment generation from text guidance is still nascent. We introduce a text-driven 3D garment generation framework, DressCode, which aims to democratize design for novices and offer immense potential in fashion design, virtual try-on, and digital human creation. For our framework, we first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance. We also tailored a pre-trained Stable Diffusion for high-quality, tile-based PBR texture generation. By leveraging a large language model, our framework generates CG-friendly garments through natural language interaction. Our method also facilitates pattern completion and texture editing, simplifying the process for designers by user-friendly interaction. With comprehensive evaluations and comparisons with other state-of-the-art methods, our method showcases the best quality and alignment with input prompts. User studies further validate our high-quality rendering results, highlighting its practical utility and potential in production settings. |
2024-04-30T00:00:00 | 2404.18928 | Stylus: Automatic Adapter Selection for Diffusion Models | [
"Michael Luo",
"Justin Wong",
"Brandon Trabucco",
"Yanping Huang",
"Joseph E. Gonzalez",
"Zhifeng Chen",
"Ruslan Salakhutdinov",
"Ion Stoica"
]
| https://github.com/stylus-diffusion/stylus | Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters have been widely adopted by open-source communities, accumulating a database of over 100K adapters-most of which are highly customized with insufficient descriptions. This paper explores the problem of matching the prompt to a set of relevant adapters, built on recent work that highlight the performance gains of composing adapters. We introduce Stylus, which efficiently selects and automatically composes task-specific adapters based on a prompt's keywords. Stylus outlines a three-stage approach that first summarizes adapters with improved descriptions and embeddings, retrieves relevant adapters, and then further assembles adapters based on prompts' keywords by checking how well they fit the prompt. To evaluate Stylus, we developed StylusDocs, a curated dataset featuring 75K adapters with pre-computed adapter embeddings. In our evaluation on popular Stable Diffusion checkpoints, Stylus achieves greater CLIP-FID Pareto efficiency and is twice as preferred, with humans and multimodal models as evaluators, over the base model. See stylus-diffusion.github.io for more. |
2024-05-01T00:00:00 | 2404.19427 | InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation | [
"Chanran Kim",
"Jeongin Lee",
"Shichang Joung",
"Bongmo Kim",
"Yeul-Min Baek"
]
| In the field of personalized image generation, the ability to create images preserving concepts has significantly improved. Creating an image that naturally integrates multiple concepts in a cohesive and visually appealing composition can indeed be challenging. This paper introduces "InstantFamily," an approach that employs a novel masked cross-attention mechanism and a multimodal embedding stack to achieve zero-shot multi-ID image generation. Our method effectively preserves ID as it utilizes global and local features from a pre-trained face recognition model integrated with text conditions. Additionally, our masked cross-attention mechanism enables the precise control of multi-ID and composition in the generated images. We demonstrate the effectiveness of InstantFamily through experiments showing its dominance in generating images with multi-ID, while resolving well-known multi-ID generation problems. Additionally, our model achieves state-of-the-art performance in both single-ID and multi-ID preservation. Furthermore, our model exhibits remarkable scalability with a greater number of ID preservation than it was originally trained with. |
|
2024-05-01T00:00:00 | 2404.19753 | DOCCI: Descriptions of Connected and Contrasting Images | [
"Yasumasa Onoe",
"Sunayana Rane",
"Zachary Berger",
"Yonatan Bitton",
"Jaemin Cho",
"Roopal Garg",
"Alexander Ku",
"Zarana Parekh",
"Jordi Pont-Tuset",
"Garrett Tanzer",
"Su Wang",
"Jason Baldridge"
]
| Vision-language datasets are vital for both text-to-image (T2I) and image-to-text (I2T) research. However, current datasets lack descriptions with fine-grained detail that would allow for richer associations to be learned by models. To fill the gap, we introduce Descriptions of Connected and Contrasting Images (DOCCI), a dataset with long, human-annotated English descriptions for 15k images that were taken, curated and donated by a single researcher intent on capturing key challenges such as spatial relations, counting, text rendering, world knowledge, and more. We instruct human annotators to create comprehensive descriptions for each image; these average 136 words in length and are crafted to clearly distinguish each image from those that are related or similar. Each description is highly compositional and typically encompasses multiple challenges. Through both quantitative and qualitative analyses, we demonstrate that DOCCI serves as an effective training resource for image-to-text generation -- a PaLI 5B model finetuned on DOCCI shows equal or superior results compared to highly-performant larger models like LLaVA-1.5 7B and InstructBLIP 7B. Furthermore, we show that DOCCI is a useful testbed for text-to-image generation, highlighting the limitations of current text-to-image models in capturing long descriptions and fine details. |
|
2024-05-01T00:00:00 | 2404.19702 | GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting | [
"Kai Zhang",
"Sai Bi",
"Hao Tan",
"Yuanbo Xiangli",
"Nanxuan Zhao",
"Kalyan Sunkavalli",
"Zexiang Xu"
]
| We propose GS-LRM, a scalable large reconstruction model that can predict high-quality 3D Gaussian primitives from 2-4 posed sparse images in 0.23 seconds on single A100 GPU. Our model features a very simple transformer-based architecture; we patchify input posed images, pass the concatenated multi-view image tokens through a sequence of transformer blocks, and decode final per-pixel Gaussian parameters directly from these tokens for differentiable rendering. In contrast to previous LRMs that can only reconstruct objects, by predicting per-pixel Gaussians, GS-LRM naturally handles scenes with large variations in scale and complexity. We show that our model can work on both object and scene captures by training it on Objaverse and RealEstate10K respectively. In both scenarios, the models outperform state-of-the-art baselines by a wide margin. We also demonstrate applications of our model in downstream 3D generation tasks. Our project webpage is available at: https://sai-bi.github.io/project/gs-lrm/ . |
|
2024-05-01T00:00:00 | 2404.19737 | Better & Faster Large Language Models via Multi-token Prediction | [
"Fabian Gloeckle",
"Badr Youbi Idrissi",
"Baptiste Rozière",
"David Lopez-Paz",
"Gabriel Synnaeve"
]
| Large language models such as GPT and Llama are trained with a next-token prediction loss. In this work, we suggest that training language models to predict multiple future tokens at once results in higher sample efficiency. More specifically, at each position in the training corpus, we ask the model to predict the following n tokens using n independent output heads, operating on top of a shared model trunk. Considering multi-token prediction as an auxiliary training task, we measure improved downstream capabilities with no overhead in training time for both code and natural language models. The method is increasingly useful for larger model sizes, and keeps its appeal when training for multiple epochs. Gains are especially pronounced on generative benchmarks like coding, where our models consistently outperform strong baselines by several percentage points. Our 13B parameter models solves 12 % more problems on HumanEval and 17 % more on MBPP than comparable next-token models. Experiments on small algorithmic tasks demonstrate that multi-token prediction is favorable for the development of induction heads and algorithmic reasoning capabilities. As an additional benefit, models trained with 4-token prediction are up to 3 times faster at inference, even with large batch sizes. |
|
2024-05-01T00:00:00 | 2404.19733 | Iterative Reasoning Preference Optimization | [
"Richard Yuanzhe Pang",
"Weizhe Yuan",
"Kyunghyun Cho",
"He He",
"Sainbayar Sukhbaatar",
"Jason Weston"
]
| Iterative preference optimization methods have recently been shown to perform well for general instruction tuning tasks, but typically make little improvement on reasoning tasks (Yuan et al., 2024, Chen et al., 2024). In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by optimizing for winning vs. losing reasoning steps that lead to the correct answer. We train using a modified DPO loss (Rafailov et al., 2023) with an additional negative log-likelihood term, which we find to be crucial. We show reasoning improves across repeated iterations of this scheme. While only relying on examples in the training set, our approach results in increasing accuracy for Llama-2-70B-Chat from 55.6% to 81.6% on GSM8K (and 88.7% with majority voting out of 32 samples), from 12.5% to 20.8% on MATH, and from 77.8% to 86.7% on ARC-Challenge, which outperforms other Llama-2-based models not relying on additionally sourced datasets. |
|
2024-05-01T00:00:00 | 2404.19149 | SAGS: Structure-Aware 3D Gaussian Splatting | [
"Evangelos Ververas",
"Rolandos Alexandros Potamias",
"Jifei Song",
"Jiankang Deng",
"Stefanos Zafeiriou"
]
| Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the way to real-time neural rendering overcoming the computational burden of volumetric methods. Following the pioneering work of 3D-GS, several methods have attempted to achieve compressible and high-fidelity performance alternatives. However, by employing a geometry-agnostic optimization scheme, these methods neglect the inherent 3D structure of the scene, thereby restricting the expressivity and the quality of the representation, resulting in various floating points and artifacts. In this work, we propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene, which reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets. SAGS is founded on a local-global graph representation that facilitates the learning of complex scenes and enforces meaningful point displacements that preserve the scene's geometry. Additionally, we introduce a lightweight version of SAGS, using a simple yet effective mid-point interpolation scheme, which showcases a compact representation of the scene with up to 24times size reduction without the reliance on any compression strategies. Extensive experiments across multiple benchmark datasets demonstrate the superiority of SAGS compared to state-of-the-art 3D-GS methods under both rendering quality and model size. Besides, we demonstrate that our structure-aware method can effectively mitigate floating artifacts and irregular distortions of previous methods while obtaining precise depth maps. Project page https://eververas.github.io/SAGS/. |
|
2024-05-01T00:00:00 | 2404.19752 | Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation | [
"Yunhao Ge",
"Xiaohui Zeng",
"Jacob Samuel Huffman",
"Tsung-Yi Lin",
"Ming-Yu Liu",
"Yin Cui"
]
| Existing automatic captioning methods for visual content face challenges such as lack of detail, content hallucination, and poor instruction following. In this work, we propose VisualFactChecker (VFC), a flexible training-free pipeline that generates high-fidelity and detailed captions for both 2D images and 3D objects. VFC consists of three steps: 1) proposal, where image-to-text captioning models propose multiple initial captions; 2) verification, where a large language model (LLM) utilizes tools such as object detection and VQA models to fact-check proposed captions; 3) captioning, where an LLM generates the final caption by summarizing caption proposals and the fact check verification results. In this step, VFC can flexibly generate captions in various styles following complex instructions. We conduct comprehensive captioning evaluations using four metrics: 1) CLIP-Score for image-text similarity; 2) CLIP-Image-Score for measuring the image-image similarity between the original and the reconstructed image generated by a text-to-image model using the caption. 3) human study on Amazon Mechanical Turk; 4) GPT-4V for fine-grained evaluation. Evaluation results show that VFC outperforms state-of-the-art open-sourced captioning methods for 2D images on the COCO dataset and 3D assets on the Objaverse dataset. Our study demonstrates that by combining open-source models into a pipeline, we can attain captioning capability comparable to proprietary models such as GPT-4V, despite being over 10x smaller in model size. |
|
2024-05-01T00:00:00 | 2404.19553 | Extending Llama-3's Context Ten-Fold Overnight | [
"Peitian Zhang",
"Ninglu Shao",
"Zheng Liu",
"Shitao Xiao",
"Hongjin Qian",
"Qiwei Ye",
"Zhicheng Dou"
]
| https://github.com/FlagOpen/FlagEmbedding | We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is super efficient, which takes 8 hours on one 8xA800 (80G) GPU machine. The resulted model exhibits superior performances across a broad range of evaluation tasks, such as NIHS, topic retrieval, and long-context language understanding; meanwhile, it also well preserves the original capability over short contexts. The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4 , which indicates the LLMs' inherent (yet largely underestimated) potential to extend its original context length. In fact, the context length could be extended far beyond 80K with more computation resources. Therefore, the team will publicly release the entire resources (including data, model, data generation pipeline, training code) so as to facilitate the future research from the community: https://github.com/FlagOpen/FlagEmbedding. |
2024-05-01T00:00:00 | 2404.19296 | Octopus v4: Graph of language models | [
"Wei Chen",
"Zhiyuan Li"
]
| https://github.com/NexaAI/octopus-v4 | Language models have been effective in a wide range of applications, yet the most sophisticated models are often proprietary. For example, GPT-4 by OpenAI and various models by Anthropic are expensive and consume substantial energy. In contrast, the open-source community has produced competitive models, like Llama3. Furthermore, niche-specific smaller language models, such as those tailored for legal, medical or financial tasks, have outperformed their proprietary counterparts. This paper introduces a novel approach that employs functional tokens to integrate multiple open-source models, each optimized for particular tasks. Our newly developed Octopus v4 model leverages functional tokens to intelligently direct user queries to the most appropriate vertical model and reformat the query to achieve the best performance. Octopus v4, an evolution of the Octopus v1, v2, and v3 models, excels in selection and parameter understanding and reformatting. Additionally, we explore the use of graph as a versatile data structure that effectively coordinates multiple open-source models by harnessing the capabilities of the Octopus model and functional tokens. Use our open-sourced GitHub (https://www.nexa4ai.com/) to try Octopus v4 models (https://huggingface.co/NexaAIDev/Octopus-v4), and contrite to a larger graph of language models. By activating models less than 10B parameters, we achieved SOTA MMLU score of 74.8 among the same level models. |
2024-05-01T00:00:00 | 2404.19759 | MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model | [
"Wenxun Dai",
"Ling-Hao Chen",
"Jingbo Wang",
"Jinpeng Liu",
"Bo Dai",
"Yansong Tang"
]
| https://github.com/Dai-Wenxun/MotionLCM | This work introduces MotionLCM, extending controllable motion generation to a real-time level. Existing methods for spatial control in text-conditioned motion generation suffer from significant runtime inefficiency. To address this issue, we first propose the motion latent consistency model (MotionLCM) for motion generation, building upon the latent diffusion model (MLD). By employing one-step (or few-step) inference, we further improve the runtime efficiency of the motion latent diffusion model for motion generation. To ensure effective controllability, we incorporate a motion ControlNet within the latent space of MotionLCM and enable explicit control signals (e.g., pelvis trajectory) in the vanilla motion space to control the generation process directly, similar to controlling other latent-free diffusion models for motion generation. By employing these techniques, our approach can generate human motions with text and control signals in real-time. Experimental results demonstrate the remarkable generation and controlling capabilities of MotionLCM while maintaining real-time runtime efficiency. |
2024-05-01T00:00:00 | 2404.19756 | KAN: Kolmogorov-Arnold Networks | [
"Ziming Liu",
"Yixuan Wang",
"Sachin Vaidya",
"Fabian Ruehle",
"James Halverson",
"Marin Soljačić",
"Thomas Y. Hou",
"Max Tegmark"
]
| Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs. |
|
2024-05-01T00:00:00 | 2404.19758 | Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting | [
"Paul Engstler",
"Andrea Vedaldi",
"Iro Laina",
"Christian Rupprecht"
]
| https://github.com/paulengstler/invisible-stitch | 3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames with existing geometry. These works often depend on pre-trained monocular depth estimators to lift the generated images into 3D, fusing them with the existing scene representation. These approaches are then often evaluated via a text metric, measuring the similarity between the generated images and a given text prompt. In this work, we make two fundamental contributions to the field of 3D scene generation. First, we note that lifting images to 3D with a monocular depth estimation model is suboptimal as it ignores the geometry of the existing scene. We thus introduce a novel depth completion model, trained via teacher distillation and self-training to learn the 3D fusion process, resulting in improved geometric coherence of the scene. Second, we introduce a new benchmarking scheme for scene generation methods that is based on ground truth geometry, and thus measures the quality of the structure of the scene. |
2024-05-01T00:00:00 | 2404.19525 | MicroDreamer: Zero-shot 3D Generation in sim20 Seconds by Score-based Iterative Reconstruction | [
"Luxi Chen",
"Zhengyi Wang",
"Chongxuan Li",
"Tingting Gao",
"Hang Su",
"Jun Zhu"
]
| https://github.com/ML-GSAI/MicroDreamer | Optimization-based approaches, such as score distillation sampling (SDS), show promise in zero-shot 3D generation but suffer from low efficiency, primarily due to the high number of function evaluations (NFEs) required for each sample. In this paper, we introduce score-based iterative reconstruction (SIR), an efficient and general algorithm for 3D generation with a multi-view score-based diffusion model. Given the images produced by the diffusion model, SIR reduces NFEs by repeatedly optimizing 3D parameters, unlike the single optimization in SDS, mimicking the 3D reconstruction process. With other improvements including optimization in the pixel space, we present an efficient approach called MicroDreamer that generally applies to various 3D representations and 3D generation tasks. In particular, retaining a comparable performance, MicroDreamer is 5-20 times faster than SDS in generating neural radiance field and takes about 20 seconds to generate meshes from 3D Gaussian splitting on a single A100 GPU, halving the time of the fastest zero-shot baseline, DreamGaussian. Our code is available at https://github.com/ML-GSAI/MicroDreamer. |
2024-05-01T00:00:00 | 2404.19760 | Lightplane: Highly-Scalable Components for Neural 3D Fields | [
"Ang Cao",
"Justin Johnson",
"Andrea Vedaldi",
"David Novotny"
]
| https://github.com/facebookresearch/lightplane | Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a significant bottleneck for existing methods and hindering new applications. In response, we propose a pair of highly scalable components for 3D neural fields: Lightplane Render and Splatter, which significantly reduce memory usage in 2D-3D mapping. These innovations enable the processing of vastly more and higher resolution images with small memory and computational costs. We demonstrate their utility in various applications, from benefiting single-scene optimization with image-level losses to realizing a versatile pipeline for dramatically scaling 3D reconstruction and generation. Code: https://github.com/facebookresearch/lightplane. |
2024-05-02T00:00:00 | 2405.00676 | Spectrally Pruned Gaussian Fields with Neural Compensation | [
"Runyi Yang",
"Zhenxin Zhu",
"Zhou Jiang",
"Baijun Ye",
"Xiaoxue Chen",
"Yifei Zhang",
"Yuantao Chen",
"Jian Zhao",
"Hao Zhao"
]
| https://github.com/RunyiYang/SUNDAE | Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered attention for its fast rendering speed and high rendering quality. However, this comes with high memory consumption, e.g., a well-trained Gaussian field may utilize three million Gaussian primitives and over 700 MB of memory. We credit this high memory footprint to the lack of consideration for the relationship between primitives. In this paper, we propose a memory-efficient Gaussian field named SUNDAE with spectral pruning and neural compensation. On one hand, we construct a graph on the set of Gaussian primitives to model their relationship and design a spectral down-sampling module to prune out primitives while preserving desired signals. On the other hand, to compensate for the quality loss of pruning Gaussians, we exploit a lightweight neural network head to mix splatted features, which effectively compensates for quality losses while capturing the relationship between primitives in its weights. We demonstrate the performance of SUNDAE with extensive results. For example, SUNDAE can achieve 26.80 PSNR at 145 FPS using 104 MB memory while the vanilla Gaussian splatting algorithm achieves 25.60 PSNR at 160 FPS using 523 MB memory, on the Mip-NeRF360 dataset. Codes are publicly available at https://runyiyang.github.io/projects/SUNDAE/. |
2024-05-02T00:00:00 | 2405.00332 | A Careful Examination of Large Language Model Performance on Grade School Arithmetic | [
"Hugh Zhang",
"Jeff Da",
"Dean Lee",
"Vaughn Robinson",
"Catherine Wu",
"Will Song",
"Tiffany Zhao",
"Pranav Raja",
"Dylan Slack",
"Qin Lyu",
"Sean Hendryx",
"Russell Kaplan",
"Michele",
"Lunati",
"Summer Yue"
]
| Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability. To investigate this claim rigorously, we commission Grade School Math 1000 (GSM1k). GSM1k is designed to mirror the style and complexity of the established GSM8k benchmark, the gold standard for measuring elementary mathematical reasoning. We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more. When evaluating leading open- and closed-source LLMs on GSM1k, we observe accuracy drops of up to 13%, with several families of models (e.g., Phi and Mistral) showing evidence of systematic overfitting across almost all model sizes. At the same time, many models, especially those on the frontier, (e.g., Gemini/GPT/Claude) show minimal signs of overfitting. Further analysis suggests a positive relationship (Spearman's r^2=0.32) between a model's probability of generating an example from GSM8k and its performance gap between GSM8k and GSM1k, suggesting that many models may have partially memorized GSM8k. |
|
2024-05-02T00:00:00 | 2405.00664 | Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3 | [
"Junsang Yoon",
"Akshat Gupta",
"Gopala Anumanchipalli"
]
| This study presents a targeted model editing analysis focused on the latest large language model, Llama-3. We explore the efficacy of popular model editing techniques - ROME, MEMIT, and EMMET, which are designed for precise layer interventions. We identify the most effective layers for targeted edits through an evaluation that encompasses up to 4096 edits across three distinct strategies: sequential editing, batch editing, and a hybrid approach we call as sequential-batch editing. Our findings indicate that increasing edit batch-sizes may degrade model performance more significantly than using smaller edit batches sequentially for equal number of edits. With this, we argue that sequential model editing is an important component for scaling model editing methods and future research should focus on methods that combine both batched and sequential editing. This observation suggests a potential limitation in current model editing methods which push towards bigger edit batch sizes, and we hope it paves way for future investigations into optimizing batch sizes and model editing performance. |
|
2024-05-02T00:00:00 | 2405.00233 | SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound | [
"Haohe Liu",
"Xuenan Xu",
"Yi Yuan",
"Mengyue Wu",
"Wenwu Wang",
"Mark D. Plumbley"
]
| Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelling techniques to audio data. However, traditional codecs often operate at high bitrates or within narrow domains such as speech and lack the semantic clues required for efficient language modelling. Addressing these challenges, we introduce SemantiCodec, a novel codec designed to compress audio into fewer than a hundred tokens per second across diverse audio types, including speech, general audio, and music, without compromising quality. SemantiCodec features a dual-encoder architecture: a semantic encoder using a self-supervised AudioMAE, discretized using k-means clustering on extensive audio data, and an acoustic encoder to capture the remaining details. The semantic and acoustic encoder outputs are used to reconstruct audio via a diffusion-model-based decoder. SemantiCodec is presented in three variants with token rates of 25, 50, and 100 per second, supporting a range of ultra-low bit rates between 0.31 kbps and 1.43 kbps. Experimental results demonstrate that SemantiCodec significantly outperforms the state-of-the-art Descript codec on reconstruction quality. Our results also suggest that SemantiCodec contains significantly richer semantic information than all evaluated audio codecs, even at significantly lower bitrates. Our code and demos are available at https://haoheliu.github.io/SemantiCodec/. |
|
2024-05-02T00:00:00 | 2404.18212 | Paint by Inpaint: Learning to Add Image Objects by Removing Them First | [
"Navve Wasserman",
"Noam Rotstein",
"Roy Ganz",
"Ron Kimmel"
]
| https://github.com/RotsteinNoam/Paint-by-Inpaint | Image editing has advanced significantly with the introduction of text-conditioned diffusion models. Despite this progress, seamlessly adding objects to images based on textual instructions without requiring user-provided input masks remains a challenge. We address this by leveraging the insight that removing objects (Inpaint) is significantly simpler than its inverse process of adding them (Paint), attributed to the utilization of segmentation mask datasets alongside inpainting models that inpaint within these masks. Capitalizing on this realization, by implementing an automated and extensive pipeline, we curate a filtered large-scale image dataset containing pairs of images and their corresponding object-removed versions. Using these pairs, we train a diffusion model to inverse the inpainting process, effectively adding objects into images. Unlike other editing datasets, ours features natural target images instead of synthetic ones; moreover, it maintains consistency between source and target by construction. Additionally, we utilize a large Vision-Language Model to provide detailed descriptions of the removed objects and a Large Language Model to convert these descriptions into diverse, natural-language instructions. We show that the trained model surpasses existing ones both qualitatively and quantitatively, and release the large-scale dataset alongside the trained models for the community. |
2024-05-02T00:00:00 | 2405.00263 | Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge | [
"Bin Xiao",
"Chunan Shi",
"Xiaonan Nie",
"Fan Yang",
"Xiangwei Deng",
"Lei Su",
"Weipeng Chen",
"Bin Cui"
]
| Large language models (LLMs) suffer from low efficiency as the mismatch between the requirement of auto-regressive decoding and the design of most contemporary GPUs. Specifically, billions to trillions of parameters must be loaded to the GPU cache through its limited memory bandwidth for computation, but only a small batch of tokens is actually computed. Consequently, the GPU spends most of its time on memory transfer instead of computation. Recently, parallel decoding, a type of speculative decoding algorithms, is becoming more popular and has demonstrated impressive efficiency improvement in generation. It introduces extra decoding heads to large models, enabling them to predict multiple subsequent tokens simultaneously and verify these candidate continuations in a single decoding step. However, this approach deviates from the training objective of next token prediction used during pre-training, resulting in a low hit rate for candidate tokens. In this paper, we propose a new speculative decoding algorithm, Clover, which integrates sequential knowledge into the parallel decoding process. This enhancement improves the hit rate of speculators and thus boosts the overall efficiency. Clover transmits the sequential knowledge from pre-speculated tokens via the Regressive Connection, then employs an Attention Decoder to integrate these speculated tokens. Additionally, Clover incorporates an Augmenting Block that modifies the hidden states to better align with the purpose of speculative generation rather than next token prediction. The experiment results demonstrate that Clover outperforms the baseline by up to 91% on Baichuan-Small and 146% on Baichuan-Large, respectively, and exceeds the performance of the previously top-performing method, Medusa, by up to 37% on Baichuan-Small and 57% on Baichuan-Large, respectively. |
|
2024-05-02T00:00:00 | 2405.00675 | Self-Play Preference Optimization for Language Model Alignment | [
"Yue Wu",
"Zhiqing Sun",
"Huizhuo Yuan",
"Kaixuan Ji",
"Yiming Yang",
"Quanquan Gu"
]
| Traditional reinforcement learning from human feedback (RLHF) approaches relying on parametric models like the Bradley-Terry model fall short in capturing the intransitivity and irrationality in human preferences. Recent advancements suggest that directly working with preference probabilities can yield a more accurate reflection of human preferences, enabling more flexible and accurate language model alignment. In this paper, we propose a self-play-based method for language model alignment, which treats the problem as a constant-sum two-player game aimed at identifying the Nash equilibrium policy. Our approach, dubbed Self-Play Preference Optimization (SPPO), approximates the Nash equilibrium through iterative policy updates and enjoys theoretical convergence guarantee. Our method can effectively increase the log-likelihood of the chosen response and decrease that of the rejected response, which cannot be trivially achieved by symmetric pairwise loss such as Direct Preference Optimization (DPO) and Identity Preference Optimization (IPO). In our experiments, using only 60k prompts (without responses) from the UltraFeedback dataset and without any prompt augmentation, by leveraging a pre-trained preference model PairRM with only 0.4B parameters, SPPO can obtain a model from fine-tuning Mistral-7B-Instruct-v0.2 that achieves the state-of-the-art length-controlled win-rate of 28.53% against GPT-4-Turbo on AlpacaEval 2.0. It also outperforms the (iterative) DPO and IPO on MT-Bench and the Open LLM Leaderboard. Notably, the strong performance of SPPO is achieved without additional external supervision (e.g., responses, preferences, etc.) from GPT-4 or other stronger language models. |
|
2024-05-02T00:00:00 | 2405.00236 | STT: Stateful Tracking with Transformers for Autonomous Driving | [
"Longlong Jing",
"Ruichi Yu",
"Xu Chen",
"Zhengli Zhao",
"Shiwei Sheng",
"Colin Graber",
"Qi Chen",
"Qinru Li",
"Shangxuan Wu",
"Han Deng",
"Sangjin Lee",
"Chris Sweeney",
"Qiurui He",
"Wei-Chih Hung",
"Tong He",
"Xingyi Zhou",
"Farshid Moussavi",
"Zijian Guo",
"Yin Zhou",
"Mingxing Tan",
"Weilong Yang",
"Congcong Li"
]
| Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTPS that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset. |
|
2024-05-02T00:00:00 | 2405.00029 | Automatic Creative Selection with Cross-Modal Matching | [
"Alex Kim",
"Jia Huang",
"Rob Monarch",
"Jerry Kwac",
"Anikesh Kamath",
"Parmeshwar Khurd",
"Kailash Thiyagarajan",
"Goodman Gu"
]
| Application developers advertise their Apps by creating product pages with App images, and bidding on search terms. It is then crucial for App images to be highly relevant with the search terms. Solutions to this problem require an image-text matching model to predict the quality of the match between the chosen image and the search terms. In this work, we present a novel approach to matching an App image to search terms based on fine-tuning a pre-trained LXMERT model. We show that compared to the CLIP model and a baseline using a Transformer model for search terms, and a ResNet model for images, we significantly improve the matching accuracy. We evaluate our approach using two sets of labels: advertiser associated (image, search term) pairs for a given application, and human ratings for the relevance between (image, search term) pairs. Our approach achieves 0.96 AUC score for advertiser associated ground truth, outperforming the transformer+ResNet baseline and the fine-tuned CLIP model by 8% and 14%. For human labeled ground truth, our approach achieves 0.95 AUC score, outperforming the transformer+ResNet baseline and the fine-tuned CLIP model by 16% and 17%. |
|
2024-05-03T00:00:00 | 2405.01434 | StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation | [
"Yupeng Zhou",
"Daquan Zhou",
"Ming-Ming Cheng",
"Jiashi Feng",
"Qibin Hou"
]
| https://github.com/HVision-NKU/StoryDiffusion | For recent diffusion-based generative models, maintaining consistent content across a series of generated images, especially those containing subjects and complex details, presents a significant challenge. In this paper, we propose a new way of self-attention calculation, termed Consistent Self-Attention, that significantly boosts the consistency between the generated images and augments prevalent pretrained diffusion-based text-to-image models in a zero-shot manner. To extend our method to long-range video generation, we further introduce a novel semantic space temporal motion prediction module, named Semantic Motion Predictor. It is trained to estimate the motion conditions between two provided images in the semantic spaces. This module converts the generated sequence of images into videos with smooth transitions and consistent subjects that are significantly more stable than the modules based on latent spaces only, especially in the context of long video generation. By merging these two novel components, our framework, referred to as StoryDiffusion, can describe a text-based story with consistent images or videos encompassing a rich variety of contents. The proposed StoryDiffusion encompasses pioneering explorations in visual story generation with the presentation of images and videos, which we hope could inspire more research from the aspect of architectural modifications. Our code is made publicly available at https://github.com/HVision-NKU/StoryDiffusion. |
2024-05-03T00:00:00 | 2405.01535 | Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models | [
"Seungone Kim",
"Juyoung Suk",
"Shayne Longpre",
"Bill Yuchen Lin",
"Jamin Shin",
"Sean Welleck",
"Graham Neubig",
"Moontae Lee",
"Kyungjae Lee",
"Minjoon Seo"
]
| https://github.com/prometheus-eval/prometheus-eval | Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from various LMs. However, concerns including transparency, controllability, and affordability strongly motivate the development of open-source LMs specialized in evaluations. On the other hand, existing open evaluator LMs exhibit critical shortcomings: 1) they issue scores that significantly diverge from those assigned by humans, and 2) they lack the flexibility to perform both direct assessment and pairwise ranking, the two most prevalent forms of assessment. Additionally, they do not possess the ability to evaluate based on custom evaluation criteria, focusing instead on general attributes like helpfulness and harmlessness. To address these issues, we introduce Prometheus 2, a more powerful evaluator LM than its predecessor that closely mirrors human and GPT-4 judgements. Moreover, it is capable of processing both direct assessment and pair-wise ranking formats grouped with a user-defined evaluation criteria. On four direct assessment benchmarks and four pairwise ranking benchmarks, Prometheus 2 scores the highest correlation and agreement with humans and proprietary LM judges among all tested open evaluator LMs. Our models, code, and data are all publicly available at https://github.com/prometheus-eval/prometheus-eval. |
2024-05-03T00:00:00 | 2405.01470 | WildChat: 1M ChatGPT Interaction Logs in the Wild | [
"Wenting Zhao",
"Xiang Ren",
"Jack Hessel",
"Claire Cardie",
"Yejin Choi",
"Yuntian Deng"
]
| Chatbots such as GPT-4 and ChatGPT are now serving millions of users. Despite their widespread use, there remains a lack of public datasets showcasing how these tools are used by a population of users in practice. To bridge this gap, we offered free access to ChatGPT for online users in exchange for their affirmative, consensual opt-in to anonymously collect their chat transcripts and request headers. From this, we compiled WildChat, a corpus of 1 million user-ChatGPT conversations, which consists of over 2.5 million interaction turns. We compare WildChat with other popular user-chatbot interaction datasets, and find that our dataset offers the most diverse user prompts, contains the largest number of languages, and presents the richest variety of potentially toxic use-cases for researchers to study. In addition to timestamped chat transcripts, we enrich the dataset with demographic data, including state, country, and hashed IP addresses, alongside request headers. This augmentation allows for more detailed analysis of user behaviors across different geographical regions and temporal dimensions. Finally, because it captures a broad range of use cases, we demonstrate the dataset's potential utility in fine-tuning instruction-following models. WildChat is released at https://wildchat.allen.ai under AI2 ImpACT Licenses. |
|
2024-05-03T00:00:00 | 2405.00732 | LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report | [
"Justin Zhao",
"Timothy Wang",
"Wael Abid",
"Geoffrey Angus",
"Arnav Garg",
"Jeffery Kinnison",
"Alex Sherstinsky",
"Piero Molino",
"Travis Addair",
"Devvret Rishi"
]
| Low Rank Adaptation (LoRA) has emerged as one of the most widely adopted methods for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs). LoRA reduces the number of trainable parameters and memory usage while achieving comparable performance to full fine-tuning. We aim to assess the viability of training and serving LLMs fine-tuned with LoRA in real-world applications. First, we measure the quality of LLMs fine-tuned with quantized low rank adapters across 10 base models and 31 tasks for a total of 310 models. We find that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. Second, we investigate the most effective base models for fine-tuning and assess the correlative and predictive capacities of task complexity heuristics in forecasting the outcomes of fine-tuning. Finally, we evaluate the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server that facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using shared base model weights and dynamic adapter loading. LoRAX powers LoRA Land, a web application that hosts 25 LoRA fine-tuned Mistral-7B LLMs on a single NVIDIA A100 GPU with 80GB memory. LoRA Land highlights the quality and cost-effectiveness of employing multiple specialized LLMs over a single, general-purpose LLM. |
|
2024-05-03T00:00:00 | 2405.01481 | NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment | [
"Gerald Shen",
"Zhilin Wang",
"Olivier Delalleau",
"Jiaqi Zeng",
"Yi Dong",
"Daniel Egert",
"Shengyang Sun",
"Jimmy Zhang",
"Sahil Jain",
"Ali Taghibakhshi",
"Markel Sanz Ausin",
"Ashwath Aithal",
"Oleksii Kuchaiev"
]
| https://github.com/NVIDIA/NeMo-Aligner | Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe. However, building efficient tools to perform alignment can be challenging, especially for the largest and most competent LLMs which often contain tens or hundreds of billions of parameters. We create NeMo-Aligner, a toolkit for model alignment that can efficiently scale to using hundreds of GPUs for training. NeMo-Aligner comes with highly optimized and scalable implementations for major paradigms of model alignment such as: Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), SteerLM, and Self-Play Fine-Tuning (SPIN). Additionally, our toolkit supports running most of the alignment techniques in a Parameter Efficient Fine-Tuning (PEFT) setting. NeMo-Aligner is designed for extensibility, allowing support for other alignment techniques with minimal effort. It is open-sourced with Apache 2.0 License and we invite community contributions at https://github.com/NVIDIA/NeMo-Aligner |
2024-05-03T00:00:00 | 2405.00983 | LLM-AD: Large Language Model based Audio Description System | [
"Peng Chu",
"Jiang Wang",
"Andre Abrantes"
]
| The development of Audio Description (AD) has been a pivotal step forward in making video content more accessible and inclusive. Traditionally, AD production has demanded a considerable amount of skilled labor, while existing automated approaches still necessitate extensive training to integrate multimodal inputs and tailor the output from a captioning style to an AD style. In this paper, we introduce an automated AD generation pipeline that harnesses the potent multimodal and instruction-following capacities of GPT-4V(ision). Notably, our methodology employs readily available components, eliminating the need for additional training. It produces ADs that not only comply with established natural language AD production standards but also maintain contextually consistent character information across frames, courtesy of a tracking-based character recognition module. A thorough analysis on the MAD dataset reveals that our approach achieves a performance on par with learning-based methods in automated AD production, as substantiated by a CIDEr score of 20.5. |
|
2024-05-03T00:00:00 | 2405.01525 | FLAME: Factuality-Aware Alignment for Large Language Models | [
"Sheng-Chieh Lin",
"Luyu Gao",
"Barlas Oguz",
"Wenhan Xiong",
"Jimmy Lin",
"Wen-tau Yih",
"Xilun Chen"
]
| Alignment is a standard procedure to fine-tune pre-trained large language models (LLMs) to follow natural language instructions and serve as helpful AI assistants. We have observed, however, that the conventional alignment process fails to enhance the factual accuracy of LLMs, and often leads to the generation of more false facts (i.e. hallucination). In this paper, we study how to make the LLM alignment process more factual, by first identifying factors that lead to hallucination in both alignment steps:\ supervised fine-tuning (SFT) and reinforcement learning (RL). In particular, we find that training the LLM on new knowledge or unfamiliar texts can encourage hallucination. This makes SFT less factual as it trains on human labeled data that may be novel to the LLM. Furthermore, reward functions used in standard RL can also encourage hallucination, because it guides the LLM to provide more helpful responses on a diverse set of instructions, often preferring longer and more detailed responses. Based on these observations, we propose factuality-aware alignment, comprised of factuality-aware SFT and factuality-aware RL through direct preference optimization. Experiments show that our proposed factuality-aware alignment guides LLMs to output more factual responses while maintaining instruction-following capability. |
|
2024-05-03T00:00:00 | 2405.01536 | Customizing Text-to-Image Models with a Single Image Pair | [
"Maxwell Jones",
"Sheng-Yu Wang",
"Nupur Kumari",
"David Bau",
"Jun-Yan Zhu"
]
| Art reinterpretation is the practice of creating a variation of a reference work, making a paired artwork that exhibits a distinct artistic style. We ask if such an image pair can be used to customize a generative model to capture the demonstrated stylistic difference. We propose Pair Customization, a new customization method that learns stylistic difference from a single image pair and then applies the acquired style to the generation process. Unlike existing methods that learn to mimic a single concept from a collection of images, our method captures the stylistic difference between paired images. This allows us to apply a stylistic change without overfitting to the specific image content in the examples. To address this new task, we employ a joint optimization method that explicitly separates the style and content into distinct LoRA weight spaces. We optimize these style and content weights to reproduce the style and content images while encouraging their orthogonality. During inference, we modify the diffusion process via a new style guidance based on our learned weights. Both qualitative and quantitative experiments show that our method can effectively learn style while avoiding overfitting to image content, highlighting the potential of modeling such stylistic differences from a single image pair. |
|
2024-05-14T00:00:00 | 2405.07990 | Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots | [
"Chengyue Wu",
"Yixiao Ge",
"Qiushan Guo",
"Jiahao Wang",
"Zhixuan Liang",
"Zeyu Lu",
"Ying Shan",
"Ping Luo"
]
| https://github.com/TencentARC/Plot2Code | The remarkable progress of Multi-modal Large Language Models (MLLMs) has attracted significant attention due to their superior performance in visual contexts. However, their capabilities in turning visual figure to executable code, have not been evaluated thoroughly. To address this, we introduce Plot2Code, a comprehensive visual coding benchmark designed for a fair and in-depth assessment of MLLMs. We carefully collect 132 manually selected high-quality matplotlib plots across six plot types from publicly available matplotlib galleries. For each plot, we carefully offer its source code, and an descriptive instruction summarized by GPT-4. This approach enables Plot2Code to extensively evaluate MLLMs' code capabilities across various input modalities. Furthermore, we propose three automatic evaluation metrics, including code pass rate, text-match ratio, and GPT-4V overall rating, for a fine-grained assessment of the output code and rendered images. Instead of simply judging pass or fail, we employ GPT-4V to make an overall judgement between the generated and reference images, which has been shown to be consistent with human evaluation. The evaluation results, which include analyses of 14 MLLMs such as the proprietary GPT-4V, Gemini-Pro, and the open-sourced Mini-Gemini, highlight the substantial challenges presented by Plot2Code. With Plot2Code, we reveal that most existing MLLMs struggle with visual coding for text-dense plots, heavily relying on textual instruction. We hope that the evaluation results from Plot2Code on visual coding will guide the future development of MLLMs. All data involved with Plot2Code are available at https://huggingface.co/datasets/TencentARC/Plot2Code. |
2024-05-14T00:00:00 | 2405.07518 | SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts | [
"Raghu Prabhakar",
"Ram Sivaramakrishnan",
"Darshan Gandhi",
"Yun Du",
"Mingran Wang",
"Xiangyu Song",
"Kejie Zhang",
"Tianren Gao",
"Angela Wang",
"Karen Li",
"Yongning Sheng",
"Joshua Brot",
"Denis Sokolov",
"Apurv Vivek",
"Calvin Leung",
"Arjun Sabnis",
"Jiayu Bai",
"Tuowen Zhao",
"Mark Gottscho",
"David Jackson",
"Mark Luttrell",
"Manish K. Shah",
"Edison Chen",
"Kaizhao Liang",
"Swayambhoo Jain",
"Urmish Thakker",
"Dawei Huang",
"Sumti Jairath",
"Kevin J. Brown",
"Kunle Olukotun"
]
| Monolithic large language models (LLMs) like GPT-4 have paved the way for modern generative AI applications. Training, serving, and maintaining monolithic LLMs at scale, however, remains prohibitively expensive and challenging. The disproportionate increase in compute-to-memory ratio of modern AI accelerators have created a memory wall, necessitating new methods to deploy AI. Composition of Experts (CoE) is an alternative modular approach that lowers the cost and complexity of training and serving. However, this approach presents two key challenges when using conventional hardware: (1) without fused operations, smaller models have lower operational intensity, which makes high utilization more challenging to achieve; and (2) hosting a large number of models can be either prohibitively expensive or slow when dynamically switching between them. In this paper, we describe how combining CoE, streaming dataflow, and a three-tier memory system scales the AI memory wall. We describe Samba-CoE, a CoE system with 150 experts and a trillion total parameters. We deploy Samba-CoE on the SambaNova SN40L Reconfigurable Dataflow Unit (RDU) - a commercial dataflow accelerator architecture that has been co-designed for enterprise inference and training applications. The chip introduces a new three-tier memory system with on-chip distributed SRAM, on-package HBM, and off-package DDR DRAM. A dedicated inter-RDU network enables scaling up and out over multiple sockets. We demonstrate speedups ranging from 2x to 13x on various benchmarks running on eight RDU sockets compared with an unfused baseline. We show that for CoE inference deployments, the 8-socket RDU Node reduces machine footprint by up to 19x, speeds up model switching time by 15x to 31x, and achieves an overall speedup of 3.7x over a DGX H100 and 6.6x over a DGX A100. |
|
2024-05-14T00:00:00 | 2405.06932 | Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training | [
"Junqin Huang",
"Zhongjie Hu",
"Zihao Jing",
"Mengya Gao",
"Yichao Wu"
]
| In this report, we introduce Piccolo2, an embedding model that surpasses other models in the comprehensive evaluation over 6 tasks on CMTEB benchmark, setting a new state-of-the-art. Piccolo2 primarily leverages an efficient multi-task hybrid loss training approach, effectively harnessing textual data and labels from diverse downstream tasks. In addition, Piccolo2 scales up the embedding dimension and uses MRL training to support more flexible vector dimensions. The latest information of piccolo models can be accessed via: https://huggingface.co/sensenova/ |
|
2024-05-14T00:00:00 | 2405.07526 | MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels | [
"Qi Chen",
"Xiubo Geng",
"Corby Rosset",
"Carolyn Buractaon",
"Jingwen Lu",
"Tao Shen",
"Kun Zhou",
"Chenyan Xiong",
"Yeyun Gong",
"Paul Bennett",
"Nick Craswell",
"Xing Xie",
"Fan Yang",
"Bryan Tower",
"Nikhil Rao",
"Anlei Dong",
"Wenqi Jiang",
"Zheng Liu",
"Mingqin Li",
"Chuanjie Liu",
"Zengzhong Li",
"Rangan Majumder",
"Jennifer Neville",
"Andy Oakley",
"Knut Magne Risvik",
"Harsha Vardhan Simhadri",
"Manik Varma",
"Yujing Wang",
"Linjun Yang",
"Mao Yang",
"Ce Zhang"
]
| https://github.com/microsoft/MS-MARCO-Web-Search | Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides rich information for various kinds of downstream tasks and encourages research in various areas, such as generic end-to-end neural indexer models, generic embedding models, and next generation information access system with large language models. MS MARCO Web Search offers a retrieval benchmark with three web retrieval challenge tasks that demand innovations in both machine learning and information retrieval system research domains. As the first dataset that meets large, real and rich data requirements, MS MARCO Web Search paves the way for future advancements in AI and system research. MS MARCO Web Search dataset is available at: https://github.com/microsoft/MS-MARCO-Web-Search. |
2024-05-14T00:00:00 | 2405.07065 | LogoMotion: Visually Grounded Code Generation for Content-Aware Animation | [
"Vivian Liu",
"Rubaiat Habib Kazi",
"Li-Yi Wei",
"Matthew Fisher",
"Timothy Langlois",
"Seth Walker",
"Lydia Chilton"
]
| Animated logos are a compelling and ubiquitous way individuals and brands represent themselves online. Manually authoring these logos can require significant artistic skill and effort. To help novice designers animate logos, design tools currently offer templates and animation presets. However, these solutions can be limited in their expressive range. Large language models have the potential to help novice designers create animated logos by generating animation code that is tailored to their content. In this paper, we introduce LogoMotion, an LLM-based system that takes in a layered document and generates animated logos through visually-grounded program synthesis. We introduce techniques to create an HTML representation of a canvas, identify primary and secondary elements, synthesize animation code, and visually debug animation errors. When compared with an industry standard tool, we find that LogoMotion produces animations that are more content-aware and are on par in terms of quality. We conclude with a discussion of the implications of LLM-generated animation for motion design. |
|
2024-05-14T00:00:00 | 2405.06650 | Large Language Models as Planning Domain Generators | [
"James Oswald",
"Kavitha Srinivas",
"Harsha Kokel",
"Junkyu Lee",
"Michael Katz",
"Shirin Sohrabi"
]
| https://github.com/IBM/NL2PDDL | Developing domain models is one of the few remaining places that require manual human labor in AI planning. Thus, in order to make planning more accessible, it is desirable to automate the process of domain model generation. To this end, we investigate if large language models (LLMs) can be used to generate planning domain models from simple textual descriptions. Specifically, we introduce a framework for automated evaluation of LLM-generated domains by comparing the sets of plans for domain instances. Finally, we perform an empirical analysis of 7 large language models, including coding and chat models across 9 different planning domains, and under three classes of natural language domain descriptions. Our results indicate that LLMs, particularly those with high parameter counts, exhibit a moderate level of proficiency in generating correct planning domains from natural language descriptions. Our code is available at https://github.com/IBM/NL2PDDL. |
2024-05-14T00:00:00 | 2405.07863 | RLHF Workflow: From Reward Modeling to Online RLHF | [
"Hanze Dong",
"Wei Xiong",
"Bo Pang",
"Haoxiang Wang",
"Han Zhao",
"Yingbo Zhou",
"Nan Jiang",
"Doyen Sahoo",
"Caiming Xiong",
"Tong Zhang"
]
| https://github.com/RLHFlow/RLHF-Reward-Modeling | We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature. However, existing open-source RLHF projects are still largely confined to the offline learning setting. In this technical report, we aim to fill in this gap and provide a detailed recipe that is easy to reproduce for online iterative RLHF. In particular, since online human feedback is usually infeasible for open-source communities with limited resources, we start by constructing preference models using a diverse set of open-source datasets and use the constructed proxy preference model to approximate human feedback. Then, we discuss the theoretical insights and algorithmic principles behind online iterative RLHF, followed by a detailed practical implementation. Our trained LLM, SFR-Iterative-DPO-LLaMA-3-8B-R, achieves impressive performance on LLM chatbot benchmarks, including AlpacaEval-2, Arena-Hard, and MT-Bench, as well as other academic benchmarks such as HumanEval and TruthfulQA. We have shown that supervised fine-tuning (SFT) and iterative RLHF can obtain state-of-the-art performance with fully open-source datasets. Further, we have made our models, curated datasets, and comprehensive step-by-step code guidebooks publicly available. Please refer to https://github.com/RLHFlow/RLHF-Reward-Modeling and https://github.com/RLHFlow/Online-RLHF for more detailed information. |
2024-05-14T00:00:00 | 2405.06694 | SUTRA: Scalable Multilingual Language Model Architecture | [
"Abhijit Bendale",
"Michael Sapienza",
"Steven Ripplinger",
"Simon Gibbs",
"Jaewon Lee",
"Pranav Mistry"
]
| In this paper, we introduce SUTRA, multilingual Large Language Model architecture capable of understanding, reasoning, and generating text in over 50 languages. SUTRA's design uniquely decouples core conceptual understanding from language-specific processing, which facilitates scalable and efficient multilingual alignment and learning. Employing a Mixture of Experts framework both in language and concept processing, SUTRA demonstrates both computational efficiency and responsiveness. Through extensive evaluations, SUTRA is demonstrated to surpass existing models like GPT-3.5, Llama2 by 20-30% on leading Massive Multitask Language Understanding (MMLU) benchmarks for multilingual tasks. SUTRA models are also online LLMs that can use knowledge from the internet to provide hallucination-free, factual and up-to-date responses while retaining their multilingual capabilities. Furthermore, we explore the broader implications of its architecture for the future of multilingual AI, highlighting its potential to democratize access to AI technology globally and to improve the equity and utility of AI in regions with predominantly non-English languages. Our findings suggest that SUTRA not only fills pivotal gaps in multilingual model capabilities but also establishes a new benchmark for operational efficiency and scalability in AI applications. |
|
2024-05-14T00:00:00 | 2405.02246 | What matters when building vision-language models? | [
"Hugo Laurençon",
"Léo Tronchon",
"Matthieu Cord",
"Victor Sanh"
]
| The growing interest in vision-language models (VLMs) has been driven by improvements in large language models and vision transformers. Despite the abundance of literature on this subject, we observe that critical decisions regarding the design of VLMs are often not justified. We argue that these unsupported decisions impede progress in the field by making it difficult to identify which choices improve model performance. To address this issue, we conduct extensive experiments around pre-trained models, architecture choice, data, and training methods. Our consolidation of findings includes the development of Idefics2, an efficient foundational VLM of 8 billion parameters. Idefics2 achieves state-of-the-art performance within its size category across various multimodal benchmarks, and is often on par with models four times its size. We release the model (base, instructed, and chat) along with the datasets created for its training. |
|
2024-05-15T00:00:00 | 2405.08246 | Compositional Text-to-Image Generation with Dense Blob Representations | [
"Weili Nie",
"Sifei Liu",
"Morteza Mardani",
"Chao Liu",
"Benjamin Eckart",
"Arash Vahdat"
]
| Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose a scene into visual primitives - denoted as dense blob representations - that contain fine-grained details of the scene while being modular, human-interpretable, and easy-to-construct. Based on blob representations, we develop a blob-grounded text-to-image diffusion model, termed BlobGEN, for compositional generation. Particularly, we introduce a new masked cross-attention module to disentangle the fusion between blob representations and visual features. To leverage the compositionality of large language models (LLMs), we introduce a new in-context learning approach to generate blob representations from text prompts. Our extensive experiments show that BlobGEN achieves superior zero-shot generation quality and better layout-guided controllability on MS-COCO. When augmented by LLMs, our method exhibits superior numerical and spatial correctness on compositional image generation benchmarks. Project page: https://blobgen-2d.github.io. |
|
2024-05-15T00:00:00 | 2405.08748 | Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding | [
"Zhimin Li",
"Jianwei Zhang",
"Qin Lin",
"Jiangfeng Xiong",
"Yanxin Long",
"Xinchi Deng",
"Yingfang Zhang",
"Xingchao Liu",
"Minbin Huang",
"Zedong Xiao",
"Dayou Chen",
"Jiajun He",
"Jiahao Li",
"Wenyue Li",
"Chen Zhang",
"Rongwei Quan",
"Jianxiang Lu",
"Jiabin Huang",
"Xiaoyan Yuan",
"Xiaoxiao Zheng",
"Yixuan Li",
"Jihong Zhang",
"Chao Zhang",
"Meng Chen",
"Jie Liu",
"Zheng Fang",
"Weiyan Wang",
"Jinbao Xue",
"Yangyu Tao",
"Jianchen Zhu",
"Kai Liu",
"Sihuan Lin",
"Yifu Sun",
"Yun Li",
"Dongdong Wang",
"Mingtao Chen",
"Zhichao Hu",
"Xiao Xiao",
"Yan Chen",
"Yuhong Liu",
"Wei Liu",
"Di Wang",
"Yong Yang",
"Jie Jiang",
"Qinglin Lu"
]
| https://github.com/Tencent/HunyuanDiT | We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-turn multimodal dialogue with users, generating and refining images according to the context. Through our holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models. Code and pretrained models are publicly available at github.com/Tencent/HunyuanDiT |
2024-05-15T00:00:00 | 2405.08054 | Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning | [
"Wenqi Dong",
"Bangbang Yang",
"Lin Ma",
"Xiao Liu",
"Liyuan Cui",
"Hujun Bao",
"Yuewen Ma",
"Zhaopeng Cui"
]
| https://github.com/zju3dv/Coin3D | As humans, we aspire to create media content that is both freely willed and readily controlled. Thanks to the prominent development of generative techniques, we now can easily utilize 2D diffusion methods to synthesize images controlled by raw sketch or designated human poses, and even progressively edit/regenerate local regions with masked inpainting. However, similar workflows in 3D modeling tasks are still unavailable due to the lack of controllability and efficiency in 3D generation. In this paper, we present a novel controllable and interactive 3D assets modeling framework, named Coin3D. Coin3D allows users to control the 3D generation using a coarse geometry proxy assembled from basic shapes, and introduces an interactive generation workflow to support seamless local part editing while delivering responsive 3D object previewing within a few seconds. To this end, we develop several techniques, including the 3D adapter that applies volumetric coarse shape control to the diffusion model, proxy-bounded editing strategy for precise part editing, progressive volume cache to support responsive preview, and volume-SDS to ensure consistent mesh reconstruction. Extensive experiments of interactive generation and editing on diverse shape proxies demonstrate that our method achieves superior controllability and flexibility in the 3D assets generation task. |
2024-05-15T00:00:00 | 2405.08344 | No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding | [
"Yingjie Zhai",
"Wenshuo Li",
"Yehui Tang",
"Xinghao Chen",
"Yunhe Wang"
]
| https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime | Current architectures for video understanding mainly build upon 3D convolutional blocks or 2D convolutions with additional operations for temporal modeling. However, these methods all regard the temporal axis as a separate dimension of the video sequence, which requires large computation and memory budgets and thus limits their usage on mobile devices. In this paper, we propose to squeeze the time axis of a video sequence into the channel dimension and present a lightweight video recognition network, term as SqueezeTime, for mobile video understanding. To enhance the temporal modeling capability of the proposed network, we design a Channel-Time Learning (CTL) Block to capture temporal dynamics of the sequence. This module has two complementary branches, in which one branch is for temporal importance learning and another branch with temporal position restoring capability is to enhance inter-temporal object modeling ability. The proposed SqueezeTime is much lightweight and fast with high accuracies for mobile video understanding. Extensive experiments on various video recognition and action detection benchmarks, i.e., Kinetics400, Kinetics600, HMDB51, AVA2.1 and THUMOS14, demonstrate the superiority of our model. For example, our SqueezeTime achieves +1.2% accuracy and +80% GPU throughput gain on Kinetics400 than prior methods. Codes are publicly available at https://github.com/xinghaochen/SqueezeTime and https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime. |
2024-05-15T00:00:00 | 2405.08707 | Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory | [
"Xueyan Niu",
"Bo Bai",
"Lei Deng",
"Wei Han"
]
| Increasing the size of a Transformer model does not always lead to enhanced performance. This phenomenon cannot be explained by the empirical scaling laws. Furthermore, improved generalization ability occurs as the model memorizes the training samples. We present a theoretical framework that sheds light on the memorization process and performance dynamics of transformer-based language models. We model the behavior of Transformers with associative memories using Hopfield networks, such that each transformer block effectively conducts an approximate nearest-neighbor search. Based on this, we design an energy function analogous to that in the modern continuous Hopfield network which provides an insightful explanation for the attention mechanism. Using the majorization-minimization technique, we construct a global energy function that captures the layered architecture of the Transformer. Under specific conditions, we show that the minimum achievable cross-entropy loss is bounded from below by a constant approximately equal to 1. We substantiate our theoretical results by conducting experiments with GPT-2 on various data sizes, as well as training vanilla Transformers on a dataset of 2M tokens. |
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