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2024-02-26T00:00:00 | 2402.15504 | Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition | [
"Chun-Hsiao Yeh",
"Ta-Ying Cheng",
"He-Yen Hsieh",
"Chuan-En Lin",
"Yi Ma",
"Andrew Markham",
"Niki Trigoni",
"H. T. Kung",
"Yubei Chen"
]
| Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examples for training. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models. First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e.g., LAION). Second, given an image containing multiple personalized concepts, there lacks a holistic metric that evaluates performance on not just the degree of resemblance of personalized concepts, but also whether all concepts are present in the image and whether the image accurately reflects the overall text description. To address these issues, we introduce Gen4Gen, a semi-automated dataset creation pipeline utilizing generative models to combine personalized concepts into complex compositions along with text-descriptions. Using this, we create a dataset called MyCanvas, that can be used to benchmark the task of multi-concept personalization. In addition, we design a comprehensive metric comprising two scores (CP-CLIP and TI-CLIP) for better quantifying the performance of multi-concept, personalized text-to-image diffusion methods. We provide a simple baseline built on top of Custom Diffusion with empirical prompting strategies for future researchers to evaluate on MyCanvas. We show that by improving data quality and prompting strategies, we can significantly increase multi-concept personalized image generation quality, without requiring any modifications to model architecture or training algorithms. |
|
2024-02-26T00:00:00 | 2402.15021 | CLoVe: Encoding Compositional Language in Contrastive Vision-Language Models | [
"Santiago Castro",
"Amir Ziai",
"Avneesh Saluja",
"Zhuoning Yuan",
"Rada Mihalcea"
]
| https://github.com/netflix/clove | Recent years have witnessed a significant increase in the performance of Vision and Language tasks. Foundational Vision-Language Models (VLMs), such as CLIP, have been leveraged in multiple settings and demonstrated remarkable performance across several tasks. Such models excel at object-centric recognition yet learn text representations that seem invariant to word order, failing to compose known concepts in novel ways. However, no evidence exists that any VLM, including large-scale single-stream models such as GPT-4V, identifies compositions successfully. In this paper, we introduce a framework to significantly improve the ability of existing models to encode compositional language, with over 10% absolute improvement on compositionality benchmarks, while maintaining or improving the performance on standard object-recognition and retrieval benchmarks. Our code and pre-trained models are publicly available at https://github.com/netflix/clove. |
2024-02-26T00:00:00 | 2402.14904 | Watermarking Makes Language Models Radioactive | [
"Tom Sander",
"Pierre Fernandez",
"Alain Durmus",
"Matthijs Douze",
"Teddy Furon"
]
| This paper investigates the radioactivity of LLM-generated texts, i.e. whether it is possible to detect that such input was used as training data. Conventional methods like membership inference can carry out this detection with some level of accuracy. We show that watermarked training data leaves traces easier to detect and much more reliable than membership inference. We link the contamination level to the watermark robustness, its proportion in the training set, and the fine-tuning process. We notably demonstrate that training on watermarked synthetic instructions can be detected with high confidence (p-value < 1e-5) even when as little as 5% of training text is watermarked. Thus, LLM watermarking, originally designed for detecting machine-generated text, gives the ability to easily identify if the outputs of a watermarked LLM were used to fine-tune another LLM. |
|
2024-02-26T00:00:00 | 2402.14848 | Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models | [
"Mosh Levy",
"Alon Jacoby",
"Yoav Goldberg"
]
| This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs). Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood. We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of input length. We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types and locations. Our findings show a notable degradation in LLMs' reasoning performance at much shorter input lengths than their technical maximum. We show that the degradation trend appears in every version of our dataset, although at different intensities. Additionally, our study reveals that traditional perplexity metrics do not correlate with performance of LLMs' in long input reasoning tasks. We analyse our results and identify failure modes that can serve as useful guides for future research, potentially informing strategies to address the limitations observed in LLMs. |
|
2024-02-26T00:00:00 | 2402.14830 | Orca-Math: Unlocking the potential of SLMs in Grade School Math | [
"Arindam Mitra",
"Hamed Khanpour",
"Corby Rosset",
"Ahmed Awadallah"
]
| Mathematical word problem-solving has long been recognized as a complex task for small language models (SLMs). A recent study hypothesized that the smallest model size, needed to achieve over 80% accuracy on the GSM8K benchmark, is 34 billion parameters. To reach this level of performance with smaller models, researcher often train SLMs to generate Python code or use tools to help avoid calculation errors. Additionally, they employ ensembling, where outputs of up to 100 model runs are combined to arrive at a more accurate result. Result selection is done using consensus, majority vote or a separate a verifier model used in conjunction with the SLM. Ensembling provides a substantial boost in accuracy but at a significant cost increase with multiple calls to the model (e.g., Phi-GSM uses top-48 to boost the performance from 68.2 to 81.5). In this work, we present Orca-Math, a 7-billion-parameter SLM based on the Mistral-7B, which achieves 86.81% on GSM8k without the need for multiple model calls or the use of verifiers, code execution or any other external tools. Our approach has the following key elements: (1) A high quality synthetic dataset of 200K math problems created using a multi-agent setup where agents collaborate to create the data, (2) An iterative learning techniques that enables the SLM to practice solving problems, receive feedback on its solutions and learn from preference pairs incorporating the SLM solutions and the feedback. When trained with Supervised Fine-Tuning alone, Orca-Math achieves 81.50% on GSM8k pass@1 metric. With iterative preference learning, Orca-Math achieves 86.81% pass@1. Orca-Math surpasses the performance of significantly larger models such as LLAMA-2-70B, WizardMath-70B, Gemini-Pro, ChatGPT-3.5. It also significantly outperforms other smaller models while using much smaller data (hundreds of thousands vs. millions of problems). |
|
2024-02-26T00:00:00 | 2402.15391 | Genie: Generative Interactive Environments | [
"Jake Bruce",
"Michael Dennis",
"Ashley Edwards",
"Jack Parker-Holder",
"Yuge Shi",
"Edward Hughes",
"Matthew Lai",
"Aditi Mavalankar",
"Richie Steigerwald",
"Chris Apps",
"Yusuf Aytar",
"Sarah Bechtle",
"Feryal Behbahani",
"Stephanie Chan",
"Nicolas Heess",
"Lucy Gonzalez",
"Simon Osindero",
"Sherjil Ozair",
"Scott Reed",
"Jingwei Zhang",
"Konrad Zolna",
"Jeff Clune",
"Nando de Freitas",
"Satinder Singh",
"Tim Rocktäschel"
]
| We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text, synthetic images, photographs, and even sketches. At 11B parameters, Genie can be considered a foundation world model. It is comprised of a spatiotemporal video tokenizer, an autoregressive dynamics model, and a simple and scalable latent action model. Genie enables users to act in the generated environments on a frame-by-frame basis despite training without any ground-truth action labels or other domain-specific requirements typically found in the world model literature. Further the resulting learned latent action space facilitates training agents to imitate behaviors from unseen videos, opening the path for training generalist agents of the future. |
|
2024-02-26T00:00:00 | 2402.15506 | AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning | [
"Jianguo Zhang",
"Tian Lan",
"Rithesh Murthy",
"Zhiwei Liu",
"Weiran Yao",
"Juntao Tan",
"Thai Hoang",
"Liangwei Yang",
"Yihao Feng",
"Zuxin Liu",
"Tulika Awalgaonkar",
"Juan Carlos Niebles",
"Silvio Savarese",
"Shelby Heinecke",
"Huan Wang",
"Caiming Xiong"
]
| Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presents inherent challenges due to the heterogeneous nature of diverse data sources featuring multi-turn trajectories. In this paper, we introduce AgentOhana as a comprehensive solution to address these challenges. AgentOhana aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training. Leveraging the data unification, our training pipeline maintains equilibrium across different data sources and preserves independent randomness across devices during dataset partitioning and model training. Additionally, we present xLAM-v0.1, a large action model tailored for AI agents, which demonstrates exceptional performance across various benchmarks. |
|
2024-02-26T00:00:00 | 2402.15491 | API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs | [
"Kinjal Basu",
"Ibrahim Abdelaziz",
"Subhajit Chaudhury",
"Soham Dan",
"Maxwell Crouse",
"Asim Munawar",
"Sadhana Kumaravel",
"Vinod Muthusamy",
"Pavan Kapanipathi",
"Luis A. Lastras"
]
| There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there is tremendous interest in methods that can acquire sufficient quantities of train and test data that involve calls to tools / APIs. Two lines of research have emerged as the predominant strategies for addressing this challenge. The first has focused on synthetic data generation techniques, while the second has involved curating task-adjacent datasets which can be transformed into API / Tool-based tasks. In this paper, we focus on the task of identifying, curating, and transforming existing datasets and, in turn, introduce API-BLEND, a large corpora for training and systematic testing of tool-augmented LLMs. The datasets mimic real-world scenarios involving API-tasks such as API / tool detection, slot filling, and sequencing of the detected APIs. We demonstrate the utility of the API-BLEND dataset for both training and benchmarking purposes. |
|
2024-02-26T00:00:00 | 2402.15319 | GPTVQ: The Blessing of Dimensionality for LLM Quantization | [
"Mart van Baalen",
"Andrey Kuzmin",
"Markus Nagel",
"Peter Couperus",
"Cedric Bastoul",
"Eric Mahurin",
"Tijmen Blankevoort",
"Paul Whatmough"
]
| In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality. We propose the GPTVQ method, a new fast method for post-training vector quantization (VQ) that scales well to Large Language Models (LLMs). Our method interleaves quantization of one or more columns with updates to the remaining unquantized weights, using information from the Hessian of the per-layer output reconstruction MSE. Quantization codebooks are initialized using an efficient data-aware version of the EM algorithm. The codebooks are then updated, and further compressed by using integer quantization and SVD-based compression. GPTVQ establishes a new state-of-the art in the size vs accuracy trade-offs on a wide range of LLMs such as Llama-v2 and Mistral. Furthermore, our method is efficient: on a single H100 it takes between 3 and 11 hours to process a Llamav2-70B model, depending on quantization setting. Lastly, with on-device timings for VQ decompression on a mobile CPU we show that VQ leads to improved latency compared to using a 4-bit integer format. |
|
2024-02-26T00:00:00 | 2402.15220 | ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition | [
"Lu Ye",
"Ze Tao",
"Yong Huang",
"Yang Li"
]
| Self-attention is an essential component of large language models(LLMs) but a significant source of inference latency for long sequences. In multi-tenant LLMs serving scenarios, the compute and memory operation cost of self-attention can be optimized by using the probability that multiple LLM requests have shared system prompts in prefixes. In this paper, we introduce ChunkAttention, a prefix-aware self-attention module that can detect matching prompt prefixes across multiple requests and share their key/value tensors in memory at runtime to improve the memory utilization of KV cache. This is achieved by breaking monolithic key/value tensors into smaller chunks and structuring them into the auxiliary prefix tree. Consequently, on top of the prefix-tree based KV cache, we design an efficient self-attention kernel, where a two-phase partition algorithm is implemented to improve the data locality during self-attention computation in the presence of shared system prompts. Experiments show that ChunkAttention can speed up the self-attention kernel by 3.2-4.8times compared to the start-of-the-art implementation, with the length of the system prompt ranging from 1024 to 4096. |
|
2024-02-26T00:00:00 | 2402.15000 | Divide-or-Conquer? Which Part Should You Distill Your LLM? | [
"Zhuofeng Wu",
"He Bai",
"Aonan Zhang",
"Jiatao Gu",
"VG Vinod Vydiswaran",
"Navdeep Jaitly",
"Yizhe Zhang"
]
| Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first. In this paper we devise a similar strategy that breaks down reasoning tasks into a problem decomposition phase and a problem solving phase and show that the strategy is able to outperform a single stage solution. Further, we hypothesize that the decomposition should be easier to distill into a smaller model compared to the problem solving because the latter requires large amounts of domain knowledge while the former only requires learning general problem solving strategies. We propose methods to distill these two capabilities and evaluate their impact on reasoning outcomes and inference cost. We find that we can distill the problem decomposition phase and at the same time achieve good generalization across tasks, datasets, and models. However, it is harder to distill the problem solving capability without losing performance and the resulting distilled model struggles with generalization. These results indicate that by using smaller, distilled problem decomposition models in combination with problem solving LLMs we can achieve reasoning with cost-efficient inference and local adaptation. |
|
2024-02-26T00:00:00 | 2402.14905 | MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases | [
"Zechun Liu",
"Changsheng Zhao",
"Forrest Iandola",
"Chen Lai",
"Yuandong Tian",
"Igor Fedorov",
"Yunyang Xiong",
"Ernie Chang",
"Yangyang Shi",
"Raghuraman Krishnamoorthi",
"Liangzhen Lai",
"Vikas Chandra"
]
| This paper addresses the growing need for efficient large language models (LLMs) on mobile devices, driven by increasing cloud costs and latency concerns. We focus on designing top-quality LLMs with fewer than a billion parameters, a practical choice for mobile deployment. Contrary to prevailing belief emphasizing the pivotal role of data and parameter quantity in determining model quality, our investigation underscores the significance of model architecture for sub-billion scale LLMs. Leveraging deep and thin architectures, coupled with embedding sharing and grouped-query attention mechanisms, we establish a strong baseline network denoted as MobileLLM, which attains a remarkable 2.7%/4.3% accuracy boost over preceding 125M/350M state-of-the-art models. Additionally, we propose an immediate block-wise weight sharing approach with no increase in model size and only marginal latency overhead. The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0.7%/0.8% than MobileLLM 125M/350M. Moreover, MobileLLM model family shows significant improvements compared to previous sub-billion models on chat benchmarks, and demonstrates close correctness to LLaMA-v2 7B in API calling tasks, highlighting the capability of small models for common on-device use cases. |
|
2024-02-26T00:00:00 | 2402.15509 | Seamless Human Motion Composition with Blended Positional Encodings | [
"German Barquero",
"Sergio Escalera",
"Cristina Palmero"
]
| Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. |
|
2024-02-27T00:00:00 | 2402.16107 | FuseChat: Knowledge Fusion of Chat Models | [
"Fanqi Wan",
"Ziyi Yang",
"Longguang Zhong",
"Xiaojun Quan",
"Xinting Huang",
"Wei Bi"
]
| https://github.com/fanqiwan/FuseLLM | While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, this approach incurs substantial costs and may lead to potential redundancy in competencies. An alternative strategy is to combine existing LLMs into a more robust LLM, thereby diminishing the necessity for expensive pre-training. However, due to the diverse architectures of LLMs, direct parameter blending proves to be unfeasible. Recently, FuseLLM introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continual training. In this report, we extend the scalability and flexibility of the FuseLLM framework to realize the fusion of chat LLMs, resulting in FuseChat. FuseChat comprises two main stages. Firstly, we undertake knowledge fusion for structurally and scale-varied source LLMs to derive multiple target LLMs of identical structure and size via lightweight fine-tuning. Then, these target LLMs are merged within the parameter space, wherein we propose a novel method for determining the merging weights based on the variation ratio of parameter matrices before and after fine-tuning. We validate our approach using three prominent chat LLMs with diverse architectures and scales, namely NH2-Mixtral-8x7B, NH2-Solar-10.7B, and OpenChat-3.5-7B. Experimental results spanning various chat domains demonstrate the superiority of \textsc{FuseChat-7B} across a broad spectrum of chat LLMs at 7B and 34B scales, even surpassing GPT-3.5 (March) and approaching Mixtral-8x7B-Instruct. Our code, model weights, and data are openly accessible at https://github.com/fanqiwan/FuseLLM. |
2024-02-27T00:00:00 | 2402.16153 | ChatMusician: Understanding and Generating Music Intrinsically with LLM | [
"Ruibin Yuan",
"Hanfeng Lin",
"Yi Wang",
"Zeyue Tian",
"Shangda Wu",
"Tianhao Shen",
"Ge Zhang",
"Yuhang Wu",
"Cong Liu",
"Ziya Zhou",
"Ziyang Ma",
"Liumeng Xue",
"Ziyu Wang",
"Qin Liu",
"Tianyu Zheng",
"Yizhi Li",
"Yinghao Ma",
"Yiming Liang",
"Xiaowei Chi",
"Ruibo Liu",
"Zili Wang",
"Pengfei Li",
"Jingcheng Wu",
"Chenghua Lin",
"Qifeng Liu",
"Tao Jiang",
"Wenhao Huang",
"Wenhu Chen",
"Emmanouil Benetos",
"Jie Fu",
"Gus Xia",
"Roger Dannenberg",
"Wei Xue",
"Shiyin Kang",
"Yike Guo"
]
| While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative language. We introduce ChatMusician, an open-source LLM that integrates intrinsic musical abilities. It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language. ChatMusician can understand and generate music with a pure text tokenizer without any external multi-modal neural structures or tokenizers. Interestingly, endowing musical abilities does not harm language abilities, even achieving a slightly higher MMLU score. Our model is capable of composing well-structured, full-length music, conditioned on texts, chords, melodies, motifs, musical forms, etc, surpassing GPT-4 baseline. On our meticulously curated college-level music understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and GPT-3.5 on zero-shot setting by a noticeable margin. Our work reveals that LLMs can be an excellent compressor for music, but there remains significant territory to be conquered. We release our 4B token music-language corpora MusicPile, the collected MusicTheoryBench, code, model and demo in GitHub. |
|
2024-02-27T00:00:00 | 2402.16843 | Multi-LoRA Composition for Image Generation | [
"Ming Zhong",
"Yelong Shen",
"Shuohang Wang",
"Yadong Lu",
"Yizhu Jiao",
"Siru Ouyang",
"Donghan Yu",
"Jiawei Han",
"Weizhu Chen"
]
| Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images. Nonetheless, existing methods face challenges in effectively composing multiple LoRAs, especially as the number of LoRAs to be integrated grows, thus hindering the creation of complex imagery. In this paper, we study multi-LoRA composition through a decoding-centric perspective. We present two training-free methods: LoRA Switch, which alternates between different LoRAs at each denoising step, and LoRA Composite, which simultaneously incorporates all LoRAs to guide more cohesive image synthesis. To evaluate the proposed approaches, we establish ComposLoRA, a new comprehensive testbed as part of this research. It features a diverse range of LoRA categories with 480 composition sets. Utilizing an evaluation framework based on GPT-4V, our findings demonstrate a clear improvement in performance with our methods over the prevalent baseline, particularly evident when increasing the number of LoRAs in a composition. |
|
2024-02-27T00:00:00 | 2402.15627 | MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs | [
"Ziheng Jiang",
"Haibin Lin",
"Yinmin Zhong",
"Qi Huang",
"Yangrui Chen",
"Zhi Zhang",
"Yanghua Peng",
"Xiang Li",
"Cong Xie",
"Shibiao Nong",
"Yulu Jia",
"Sun He",
"Hongmin Chen",
"Zhihao Bai",
"Qi Hou",
"Shipeng Yan",
"Ding Zhou",
"Yiyao Sheng",
"Zhuo Jiang",
"Haohan Xu",
"Haoran Wei",
"Zhang Zhang",
"Pengfei Nie",
"Leqi Zou",
"Sida Zhao",
"Liang Xiang",
"Zherui Liu",
"Zhe Li",
"Xiaoying Jia",
"Jianxi Ye",
"Xin Jin",
"Xin Liu"
]
| We present the design, implementation and engineering experience in building and deploying MegaScale, a production system for training large language models (LLMs) at the scale of more than 10,000 GPUs. Training LLMs at this scale brings unprecedented challenges to training efficiency and stability. We take a full-stack approach that co-designs the algorithmic and system components across model block and optimizer design, computation and communication overlapping, operator optimization, data pipeline, and network performance tuning. Maintaining high efficiency throughout the training process (i.e., stability) is an important consideration in production given the long extent of LLM training jobs. Many hard stability issues only emerge at large scale, and in-depth observability is the key to address them. We develop a set of diagnosis tools to monitor system components and events deep in the stack, identify root causes, and derive effective techniques to achieve fault tolerance and mitigate stragglers. MegaScale achieves 55.2% Model FLOPs Utilization (MFU) when training a 175B LLM model on 12,288 GPUs, improving the MFU by 1.34x compared to Megatron-LM. We share our operational experience in identifying and fixing failures and stragglers. We hope by articulating the problems and sharing our experience from a systems perspective, this work can inspire future LLM systems research. |
|
2024-02-27T00:00:00 | 2402.16840 | MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT | [
"Omkar Thawakar",
"Ashmal Vayani",
"Salman Khan",
"Hisham Cholakal",
"Rao M. Anwer",
"Michael Felsberg",
"Tim Baldwin",
"Eric P. Xing",
"Fahad Shahbaz Khan"
]
| https://github.com/mbzuai-oryx/MobiLlama | "Bigger the better" has been the predominant trend in recent Large Language Models (LLMs) development. However, LLMs do not suit well for scenarios that require on-device processing, energy efficiency, low memory footprint, and response efficiency. These requisites are crucial for privacy, security, and sustainable deployment. This paper explores the "less is more" paradigm by addressing the challenge of designing accurate yet efficient Small Language Models (SLMs) for resource constrained devices. Our primary contribution is the introduction of an accurate and fully transparent open-source 0.5 billion (0.5B) parameter SLM, named MobiLlama, catering to the specific needs of resource-constrained computing with an emphasis on enhanced performance with reduced resource demands. MobiLlama is a SLM design that initiates from a larger model and applies a careful parameter sharing scheme to reduce both the pre-training and the deployment cost. Our work strives to not only bridge the gap in open-source SLMs but also ensures full transparency, where complete training data pipeline, training code, model weights, and over 300 checkpoints along with evaluation codes is available at : https://github.com/mbzuai-oryx/MobiLlama. |
2024-02-27T00:00:00 | 2402.16671 | StructLM: Towards Building Generalist Models for Structured Knowledge Grounding | [
"Alex Zhuang",
"Ge Zhang",
"Tianyu Zheng",
"Xinrun Du",
"Junjie Wang",
"Weiming Ren",
"Stephen W. Huang",
"Jie Fu",
"Xiang Yue",
"Wenhu Chen"
]
| Structured data sources, such as tables, graphs, and databases, are ubiquitous knowledge sources. Despite the demonstrated capabilities of large language models (LLMs) on plain text, their proficiency in interpreting and utilizing structured data remains limited. Our investigation reveals a notable deficiency in LLMs' ability to process structured data, e.g., ChatGPT lags behind state-of-the-art (SoTA) model by an average of 35%. To augment the Structured Knowledge Grounding (SKG) capabilities in LLMs, we have developed a comprehensive instruction tuning dataset comprising 1.1 million examples. Utilizing this dataset, we train a series of models, referred to as StructLM, based on the Code-LLaMA architecture, ranging from 7B to 34B parameters. Our StructLM series surpasses task-specific models on 14 out of 18 evaluated datasets and establishes new SoTA achievements on 7 SKG tasks. Furthermore, StructLM demonstrates exceptional generalization across 6 novel SKG tasks. Contrary to expectations, we observe that scaling model size offers marginal benefits, with StructLM-34B showing only slight improvements over StructLM-7B. This suggests that structured knowledge grounding is still a challenging task and requires more innovative design to push to a new level. |
|
2024-02-27T00:00:00 | 2402.16837 | Do Large Language Models Latently Perform Multi-Hop Reasoning? | [
"Sohee Yang",
"Elena Gribovskaya",
"Nora Kassner",
"Mor Geva",
"Sebastian Riedel"
]
| We study whether Large Language Models (LLMs) latently perform multi-hop reasoning with complex prompts such as "The mother of the singer of 'Superstition' is". We look for evidence of a latent reasoning pathway where an LLM (1) latently identifies "the singer of 'Superstition'" as Stevie Wonder, the bridge entity, and (2) uses its knowledge of Stevie Wonder's mother to complete the prompt. We analyze these two hops individually and consider their co-occurrence as indicative of latent multi-hop reasoning. For the first hop, we test if changing the prompt to indirectly mention the bridge entity instead of any other entity increases the LLM's internal recall of the bridge entity. For the second hop, we test if increasing this recall causes the LLM to better utilize what it knows about the bridge entity. We find strong evidence of latent multi-hop reasoning for the prompts of certain relation types, with the reasoning pathway used in more than 80% of the prompts. However, the utilization is highly contextual, varying across different types of prompts. Also, on average, the evidence for the second hop and the full multi-hop traversal is rather moderate and only substantial for the first hop. Moreover, we find a clear scaling trend with increasing model size for the first hop of reasoning but not for the second hop. Our experimental findings suggest potential challenges and opportunities for future development and applications of LLMs. |
|
2024-02-27T00:00:00 | 2402.16641 | Towards Open-ended Visual Quality Comparison | [
"Haoning Wu",
"Hanwei Zhu",
"Zicheng Zhang",
"Erli Zhang",
"Chaofeng Chen",
"Liang Liao",
"Chunyi Li",
"Annan Wang",
"Wenxiu Sun",
"Qiong Yan",
"Xiaohong Liu",
"Guangtao Zhai",
"Shiqi Wang",
"Weisi Lin"
]
| Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses. In this work, we extend the edge of emerging large multi-modality models (LMMs) to further advance visual quality comparison into open-ended settings, that 1) can respond to open-range questions on quality comparison; 2) can provide detailed reasonings beyond direct answers. To this end, we propose the Co-Instruct. To train this first-of-its-kind open-source open-ended visual quality comparer, we collect the Co-Instruct-562K dataset, from two sources: (a) LMM-merged single image quality description, (b) GPT-4V "teacher" responses on unlabeled data. Furthermore, to better evaluate this setting, we propose the MICBench, the first benchmark on multi-image comparison for LMMs. We demonstrate that Co-Instruct not only achieves 30% higher superior accuracy than state-of-the-art open-source LMMs, but also outperforms GPT-4V (its teacher), on both existing related benchmarks and the proposed MICBench. Our model is published at https://huggingface.co/q-future/co-instruct. |
|
2024-02-27T00:00:00 | 2402.16822 | Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts | [
"Mikayel Samvelyan",
"Sharath Chandra Raparthy",
"Andrei Lupu",
"Eric Hambro",
"Aram H. Markosyan",
"Manish Bhatt",
"Yuning Mao",
"Minqi Jiang",
"Jack Parker-Holder",
"Jakob Foerster",
"Tim Rocktäschel",
"Roberta Raileanu"
]
| As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to user inputs is of paramount importance. Existing methods for identifying adversarial prompts tend to focus on specific domains, lack diversity, or require extensive human annotations. To address these limitations, we present Rainbow Teaming, a novel approach for producing a diverse collection of adversarial prompts. Rainbow Teaming casts adversarial prompt generation as a quality-diversity problem, and uses open-ended search to generate prompts that are both effective and diverse. It can uncover a model's vulnerabilities across a broad range of domains including, in this paper, safety, question answering, and cybersecurity. We also demonstrate that fine-tuning on synthetic data generated by Rainbow Teaming improves the safety of state-of-the-art LLMs without hurting their general capabilities and helpfulness, paving the path to open-ended self-improvement. |
|
2024-02-27T00:00:00 | 2402.16819 | Nemotron-4 15B Technical Report | [
"Jupinder Parmar",
"Shrimai Prabhumoye",
"Joseph Jennings",
"Mostofa Patwary",
"Sandeep Subramanian",
"Dan Su",
"Chen Zhu",
"Deepak Narayanan",
"Aastha Jhunjhunwala",
"Ayush Dattagupta",
"Vibhu Jawa",
"Jiwei Liu",
"Ameya Mahabaleshwarkar",
"Osvald Nitski",
"Annika Brundyn",
"James Maki",
"Miguel Martinez",
"Jiaxuan You",
"John Kamalu",
"Patrick LeGresley",
"Denys Fridman",
"Jared Casper",
"Ashwath Aithal",
"Oleksii Kuchaiev",
"Mohammad Shoeybi",
"Jonathan Cohen",
"Bryan Catanzaro"
]
| We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms all existing similarly-sized open models on 4 out of 7 downstream evaluation areas and achieves competitive performance to the leading open models in the remaining ones. Specifically, Nemotron-4 15B exhibits the best multilingual capabilities of all similarly-sized models, even outperforming models over four times larger and those explicitly specialized for multilingual tasks. |
|
2024-02-28T00:00:00 | 2402.17177 | Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models | [
"Yixin Liu",
"Kai Zhang",
"Yuan Li",
"Zhiling Yan",
"Chujie Gao",
"Ruoxi Chen",
"Zhengqing Yuan",
"Yue Huang",
"Hanchi Sun",
"Jianfeng Gao",
"Lifang He",
"Lichao Sun"
]
| Sora is a text-to-video generative AI model, released by OpenAI in February 2024. The model is trained to generate videos of realistic or imaginative scenes from text instructions and show potential in simulating the physical world. Based on public technical reports and reverse engineering, this paper presents a comprehensive review of the model's background, related technologies, applications, remaining challenges, and future directions of text-to-video AI models. We first trace Sora's development and investigate the underlying technologies used to build this "world simulator". Then, we describe in detail the applications and potential impact of Sora in multiple industries ranging from film-making and education to marketing. We discuss the main challenges and limitations that need to be addressed to widely deploy Sora, such as ensuring safe and unbiased video generation. Lastly, we discuss the future development of Sora and video generation models in general, and how advancements in the field could enable new ways of human-AI interaction, boosting productivity and creativity of video generation. |
|
2024-02-28T00:00:00 | 2402.17139 | Video as the New Language for Real-World Decision Making | [
"Sherry Yang",
"Jacob Walker",
"Jack Parker-Holder",
"Yilun Du",
"Jake Bruce",
"Andre Barreto",
"Pieter Abbeel",
"Dale Schuurmans"
]
| Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, whereas video generation has remained largely limited to media entertainment. Yet video data captures important information about the physical world that is difficult to express in language. To address this gap, we discuss an under-appreciated opportunity to extend video generation to solve tasks in the real world. We observe how, akin to language, video can serve as a unified interface that can absorb internet knowledge and represent diverse tasks. Moreover, we demonstrate how, like language models, video generation can serve as planners, agents, compute engines, and environment simulators through techniques such as in-context learning, planning and reinforcement learning. We identify major impact opportunities in domains such as robotics, self-driving, and science, supported by recent work that demonstrates how such advanced capabilities in video generation are plausibly within reach. Lastly, we identify key challenges in video generation that mitigate progress. Addressing these challenges will enable video generation models to demonstrate unique value alongside language models in a wider array of AI applications. |
|
2024-02-28T00:00:00 | 2402.17485 | EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions | [
"Linrui Tian",
"Qi Wang",
"Bang Zhang",
"Liefeng Bo"
]
| In this work, we tackle the challenge of enhancing the realism and expressiveness in talking head video generation by focusing on the dynamic and nuanced relationship between audio cues and facial movements. We identify the limitations of traditional techniques that often fail to capture the full spectrum of human expressions and the uniqueness of individual facial styles. To address these issues, we propose EMO, a novel framework that utilizes a direct audio-to-video synthesis approach, bypassing the need for intermediate 3D models or facial landmarks. Our method ensures seamless frame transitions and consistent identity preservation throughout the video, resulting in highly expressive and lifelike animations. Experimental results demonsrate that EMO is able to produce not only convincing speaking videos but also singing videos in various styles, significantly outperforming existing state-of-the-art methodologies in terms of expressiveness and realism. |
|
2024-02-28T00:00:00 | 2402.17403 | Sora Generates Videos with Stunning Geometrical Consistency | [
"Xuanyi Li",
"Daquan Zhou",
"Chenxu Zhang",
"Shaodong Wei",
"Qibin Hou",
"Ming-Ming Cheng"
]
| The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena. Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively. In this paper, we introduce a new benchmark that assesses the quality of the generated videos based on their adherence to real-world physics principles. We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality. From the perspective of 3D reconstruction, we use the fidelity of the geometric constraints satisfied by the constructed 3D models as a proxy to gauge the extent to which the generated videos conform to real-world physics rules. Project page: https://sora-geometrical-consistency.github.io/ |
|
2024-02-28T00:00:00 | 2402.17553 | OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web | [
"Raghav Kapoor",
"Yash Parag Butala",
"Melisa Russak",
"Jing Yu Koh",
"Kiran Kamble",
"Waseem Alshikh",
"Ruslan Salakhutdinov"
]
| For decades, human-computer interaction has fundamentally been manual. Even today, almost all productive work done on the computer necessitates human input at every step. Autonomous virtual agents represent an exciting step in automating many of these menial tasks. Virtual agents would empower users with limited technical proficiency to harness the full possibilities of computer systems. They could also enable the efficient streamlining of numerous computer tasks, ranging from calendar management to complex travel bookings, with minimal human intervention. In this paper, we introduce OmniACT, the first-of-a-kind dataset and benchmark for assessing an agent's capability to generate executable programs to accomplish computer tasks. Our scope extends beyond traditional web automation, covering a diverse range of desktop applications. The dataset consists of fundamental tasks such as "Play the next song", as well as longer horizon tasks such as "Send an email to John Doe mentioning the time and place to meet". Specifically, given a pair of screen image and a visually-grounded natural language task, the goal is to generate a script capable of fully executing the task. We run several strong baseline language model agents on our benchmark. The strongest baseline, GPT-4, performs the best on our benchmark However, its performance level still reaches only 15% of the human proficiency in generating executable scripts capable of completing the task, demonstrating the challenge of our task for conventional web agents. Our benchmark provides a platform to measure and evaluate the progress of language model agents in automating computer tasks and motivates future work towards building multimodal models that bridge large language models and the visual grounding of computer screens. |
|
2024-02-28T00:00:00 | 2402.17764 | The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits | [
"Shuming Ma",
"Hongyu Wang",
"Lingxiao Ma",
"Lei Wang",
"Wenhui Wang",
"Shaohan Huang",
"Li Dong",
"Ruiping Wang",
"Jilong Xue",
"Furu Wei"
]
| Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs. |
|
2024-02-28T00:00:00 | 2402.17723 | Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners | [
"Yazhou Xing",
"Yingqing He",
"Zeyue Tian",
"Xintao Wang",
"Qifeng Chen"
]
| Video and audio content creation serves as the core technique for the movie industry and professional users. Recently, existing diffusion-based methods tackle video and audio generation separately, which hinders the technique transfer from academia to industry. In this work, we aim at filling the gap, with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation. We observe the powerful generation ability of off-the-shelf video or audio generation models. Thus, instead of training the giant models from scratch, we propose to bridge the existing strong models with a shared latent representation space. Specifically, we propose a multimodality latent aligner with the pre-trained ImageBind model. Our latent aligner shares a similar core as the classifier guidance that guides the diffusion denoising process during inference time. Through carefully designed optimization strategy and loss functions, we show the superior performance of our method on joint video-audio generation, visual-steered audio generation, and audio-steered visual generation tasks. The project website can be found at https://yzxing87.github.io/Seeing-and-Hearing/ |
|
2024-02-28T00:00:00 | 2402.17759 | Towards Optimal Learning of Language Models | [
"Yuxian Gu",
"Li Dong",
"Yaru Hao",
"Qingxiu Dong",
"Minlie Huang",
"Furu Wei"
]
| This work studies the general principles of improving the learning of language models (LMs), which aims at reducing the necessary training steps for achieving superior performance. Specifically, we present a theory for the optimal learning of LMs. We first propose an objective that optimizes LM learning by maximizing the data compression ratio in an "LM-training-as-lossless-compression" view. Then, we derive a theorem, named Learning Law, to reveal the properties of the dynamics in the optimal learning process under our objective. The theorem is then validated by experiments on a linear classification and a real-world language modeling task. Finally, we empirically verify that the optimal learning of LMs essentially stems from the improvement of the coefficients in the scaling law of LMs, indicating great promise and significance for designing practical learning acceleration methods. Our code can be found at https://aka.ms/LearningLaw. |
|
2024-02-28T00:00:00 | 2402.16936 | Disentangled 3D Scene Generation with Layout Learning | [
"Dave Epstein",
"Ben Poole",
"Ben Mildenhall",
"Alexei A. Efros",
"Aleksander Holynski"
]
| We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretrained text-to-image model. Our key insight is that objects can be discovered by finding parts of a 3D scene that, when rearranged spatially, still produce valid configurations of the same scene. Concretely, our method jointly optimizes multiple NeRFs from scratch - each representing its own object - along with a set of layouts that composite these objects into scenes. We then encourage these composited scenes to be in-distribution according to the image generator. We show that despite its simplicity, our approach successfully generates 3D scenes decomposed into individual objects, enabling new capabilities in text-to-3D content creation. For results and an interactive demo, see our project page at https://dave.ml/layoutlearning/ |
|
2024-02-28T00:00:00 | 2402.17193 | When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method | [
"Biao Zhang",
"Zhongtao Liu",
"Colin Cherry",
"Orhan Firat"
]
| While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scaling properties) of different finetuning methods is still limited. To fill this gap, we conduct systematic experiments studying whether and how different scaling factors, including LLM model size, pretraining data size, new finetuning parameter size and finetuning data size, affect the finetuning performance. We consider two types of finetuning -- full-model tuning (FMT) and parameter efficient tuning (PET, including prompt tuning and LoRA), and explore their scaling behaviors in the data-limited regime where the LLM model size substantially outweighs the finetuning data size. Based on two sets of pretrained bilingual LLMs from 1B to 16B and experiments on bilingual machine translation and multilingual summarization benchmarks, we find that 1) LLM finetuning follows a powerbased multiplicative joint scaling law between finetuning data size and each other scaling factor; 2) LLM finetuning benefits more from LLM model scaling than pretraining data scaling, and PET parameter scaling is generally ineffective; and 3) the optimal finetuning method is highly task- and finetuning data-dependent. We hope our findings could shed light on understanding, selecting and developing LLM finetuning methods. |
|
2024-02-28T00:00:00 | 2402.17245 | Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation | [
"Daiqing Li",
"Aleks Kamko",
"Ehsan Akhgari",
"Ali Sabet",
"Linmiao Xu",
"Suhail Doshi"
]
| In this work, we share three insights for achieving state-of-the-art aesthetic quality in text-to-image generative models. We focus on three critical aspects for model improvement: enhancing color and contrast, improving generation across multiple aspect ratios, and improving human-centric fine details. First, we delve into the significance of the noise schedule in training a diffusion model, demonstrating its profound impact on realism and visual fidelity. Second, we address the challenge of accommodating various aspect ratios in image generation, emphasizing the importance of preparing a balanced bucketed dataset. Lastly, we investigate the crucial role of aligning model outputs with human preferences, ensuring that generated images resonate with human perceptual expectations. Through extensive analysis and experiments, Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic quality under various conditions and aspect ratios, outperforming both widely-used open-source models like SDXL and Playground v2, and closed-source commercial systems such as DALLE 3 and Midjourney v5.2. Our model is open-source, and we hope the development of Playground v2.5 provides valuable guidelines for researchers aiming to elevate the aesthetic quality of diffusion-based image generation models. |
|
2024-02-28T00:00:00 | 2402.17412 | DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Model | [
"Shyam Marjit",
"Harshit Singh",
"Nityanand Mathur",
"Sayak Paul",
"Chia-Mu Yu",
"Pin-Yu Chen"
]
| https://github.com/IBM/DiffuseKronA | In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements. While the low-rank adaptation (LoRA) module within DreamBooth offers a reduction in trainable parameters, it introduces a pronounced sensitivity to hyperparameters, leading to a compromise between parameter efficiency and the quality of T2I personalized image synthesis. Addressing these constraints, we introduce \textit{DiffuseKronA}, a novel Kronecker product-based adaptation module that not only significantly reduces the parameter count by 35\% and 99.947\% compared to LoRA-DreamBooth and the original DreamBooth, respectively, but also enhances the quality of image synthesis. Crucially, DiffuseKronA mitigates the issue of hyperparameter sensitivity, delivering consistent high-quality generations across a wide range of hyperparameters, thereby diminishing the necessity for extensive fine-tuning. Furthermore, a more controllable decomposition makes DiffuseKronA more interpretable and even can achieve up to a 50\% reduction with results comparable to LoRA-Dreambooth. Evaluated against diverse and complex input images and text prompts, DiffuseKronA consistently outperforms existing models, producing diverse images of higher quality with improved fidelity and a more accurate color distribution of objects, all the while upholding exceptional parameter efficiency, thus presenting a substantial advancement in the field of T2I generative modeling. Our project page, consisting of links to the code, and pre-trained checkpoints, is available at https://diffusekrona.github.io/{https://diffusekrona.github.io/}. |
2024-02-28T00:00:00 | 2402.17753 | Evaluating Very Long-Term Conversational Memory of LLM Agents | [
"Adyasha Maharana",
"Dong-Ho Lee",
"Sergey Tulyakov",
"Mohit Bansal",
"Francesco Barbieri",
"Yuwei Fang"
]
| Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models (LLMs) and retrieval augmented generation (RAG) techniques, their efficacy in very long-term dialogues remains unexplored. To address this research gap, we introduce a machine-human pipeline to generate high-quality, very long-term dialogues by leveraging LLM-based agent architectures and grounding their dialogues on personas and temporal event graphs. Moreover, we equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. Using this pipeline, we collect LoCoMo, a dataset of very long-term conversations, each encompassing 300 turns and 9K tokens on avg., over up to 35 sessions. Based on LoCoMo, we present a comprehensive evaluation benchmark to measure long-term memory in models, encompassing question answering, event summarization, and multi-modal dialogue generation tasks. Our experimental results indicate that LLMs exhibit challenges in understanding lengthy conversations and comprehending long-range temporal and causal dynamics within dialogues. Employing strategies like long-context LLMs or RAG can offer improvements but these models still substantially lag behind human performance. |
|
2024-02-28T00:00:00 | 2402.17463 | Training-Free Long-Context Scaling of Large Language Models | [
"Chenxin An",
"Fei Huang",
"Jun Zhang",
"Shansan Gong",
"Xipeng Qiu",
"Chang Zhou",
"Lingpeng Kong"
]
| https://github.com/HKUNLP/ChunkLlama | The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the expensive overhead of finetuning large-scale models with longer sequences, we propose Dual Chunk Attention (DCA), which enables Llama2 70B to support context windows of more than 100k tokens without continual training. By decomposing the attention computation for long sequences into chunk-based modules, DCA manages to effectively capture the relative positional information of tokens within the same chunk (Intra-Chunk) and across distinct chunks (Inter-Chunk), as well as integrates seamlessly with Flash Attention. In addition to its impressive extrapolation capability, DCA achieves performance on practical long-context tasks that is comparable to or even better than that of finetuned models. When compared with proprietary models, our training-free 70B model attains 94% of the performance of gpt-3.5-16k, indicating it is a viable open-source alternative. All code and data used in this work are released at https://github.com/HKUNLP/ChunkLlama. |
2024-02-28T00:00:00 | 2402.17427 | VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction | [
"Jiaqi Lin",
"Zhihao Li",
"Xiao Tang",
"Jianzhuang Liu",
"Shiyong Liu",
"Jiayue Liu",
"Yangdi Lu",
"Xiaofei Wu",
"Songcen Xu",
"Youliang Yan",
"Wenming Yang"
]
| Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes, scaling it up to large scenes poses challenges due to limited video memory, long optimization time, and noticeable appearance variations. To address these challenges, we present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells, where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets, enabling fast optimization and high-fidelity real-time rendering. |
|
2024-03-01T00:00:00 | 2402.19479 | Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers | [
"Tsai-Shien Chen",
"Aliaksandr Siarohin",
"Willi Menapace",
"Ekaterina Deyneka",
"Hsiang-wei Chao",
"Byung Eun Jeon",
"Yuwei Fang",
"Hsin-Ying Lee",
"Jian Ren",
"Ming-Hsuan Yang",
"Sergey Tulyakov"
]
| https://github.com/snap-research/Panda-70M | The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consuming, as it requires an annotator to watch an entire video. Second, videos have a temporal dimension, consisting of several scenes stacked together, and showing multiple actions. Accordingly, to establish a video dataset with high-quality captions, we propose an automatic approach leveraging multimodal inputs, such as textual video description, subtitles, and individual video frames. Specifically, we curate 3.8M high-resolution videos from the publicly available HD-VILA-100M dataset. We then split them into semantically consistent video clips, and apply multiple cross-modality teacher models to obtain captions for each video. Next, we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation. In this way, we get 70M videos paired with high-quality text captions. We dub the dataset as Panda-70M. We show the value of the proposed dataset on three downstream tasks: video captioning, video and text retrieval, and text-driven video generation. The models trained on the proposed data score substantially better on the majority of metrics across all the tasks. |
2024-03-01T00:00:00 | 2402.18842 | ViewFusion: Towards Multi-View Consistency via Interpolated Denoising | [
"Xianghui Yang",
"Yan Zuo",
"Sameera Ramasinghe",
"Loris Bazzani",
"Gil Avraham",
"Anton van den Hengel"
]
| Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet, the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this, we introduce ViewFusion, a novel, training-free algorithm that can be seamlessly integrated into existing pre-trained diffusion models. Our approach adopts an auto-regressive method that implicitly leverages previously generated views as context for the next view generation, ensuring robust multi-view consistency during the novel-view generation process. Through a diffusion process that fuses known-view information via interpolated denoising, our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning. Extensive experimental results demonstrate the effectiveness of ViewFusion in generating consistent and detailed novel views. |
|
2024-03-01T00:00:00 | 2402.19427 | Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models | [
"Soham De",
"Samuel L. Smith",
"Anushan Fernando",
"Aleksandar Botev",
"George Cristian-Muraru",
"Albert Gu",
"Ruba Haroun",
"Leonard Berrada",
"Yutian Chen",
"Srivatsan Srinivasan",
"Guillaume Desjardins",
"Arnaud Doucet",
"David Budden",
"Yee Whye Teh",
"Razvan Pascanu",
"Nando De Freitas",
"Caglar Gulcehre"
]
| Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated linear recurrences, and Griffin, a hybrid model that mixes gated linear recurrences with local attention. Hawk exceeds the reported performance of Mamba on downstream tasks, while Griffin matches the performance of Llama-2 despite being trained on over 6 times fewer tokens. We also show that Griffin can extrapolate on sequences significantly longer than those seen during training. Our models match the hardware efficiency of Transformers during training, and during inference they have lower latency and significantly higher throughput. We scale Griffin up to 14B parameters, and explain how to shard our models for efficient distributed training. |
|
2024-03-01T00:00:00 | 2402.19155 | Beyond Language Models: Byte Models are Digital World Simulators | [
"Shangda Wu",
"Xu Tan",
"Zili Wang",
"Rui Wang",
"Xiaobing Li",
"Maosong Sun"
]
| Traditional deep learning often overlooks bytes, the basic units of the digital world, where all forms of information and operations are encoded and manipulated in binary format. Inspired by the success of next token prediction in natural language processing, we introduce bGPT, a model with next byte prediction to simulate the digital world. bGPT matches specialized models in performance across various modalities, including text, audio, and images, and offers new possibilities for predicting, simulating, and diagnosing algorithm or hardware behaviour. It has almost flawlessly replicated the process of converting symbolic music data, achieving a low error rate of 0.0011 bits per byte in converting ABC notation to MIDI format. In addition, bGPT demonstrates exceptional capabilities in simulating CPU behaviour, with an accuracy exceeding 99.99% in executing various operations. Leveraging next byte prediction, models like bGPT can directly learn from vast binary data, effectively simulating the intricate patterns of the digital world. |
|
2024-03-01T00:00:00 | 2402.19469 | Humanoid Locomotion as Next Token Prediction | [
"Ilija Radosavovic",
"Bike Zhang",
"Baifeng Shi",
"Jathushan Rajasegaran",
"Sarthak Kamat",
"Trevor Darrell",
"Koushil Sreenath",
"Jitendra Malik"
]
| We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive prediction of sensorimotor trajectories. To account for the multi-modal nature of the data, we perform prediction in a modality-aligned way, and for each input token predict the next token from the same modality. This general formulation enables us to leverage data with missing modalities, like video trajectories without actions. We train our model on a collection of simulated trajectories coming from prior neural network policies, model-based controllers, motion capture data, and YouTube videos of humans. We show that our model enables a full-sized humanoid to walk in San Francisco zero-shot. Our model can transfer to the real world even when trained on only 27 hours of walking data, and can generalize to commands not seen during training like walking backward. These findings suggest a promising path toward learning challenging real-world control tasks by generative modeling of sensorimotor trajectories. |
|
2024-03-01T00:00:00 | 2402.19173 | StarCoder 2 and The Stack v2: The Next Generation | [
"Anton Lozhkov",
"Raymond Li",
"Loubna Ben Allal",
"Federico Cassano",
"Joel Lamy-Poirier",
"Nouamane Tazi",
"Ao Tang",
"Dmytro Pykhtar",
"Jiawei Liu",
"Yuxiang Wei",
"Tianyang Liu",
"Max Tian",
"Denis Kocetkov",
"Arthur Zucker",
"Younes Belkada",
"Zijian Wang",
"Qian Liu",
"Dmitry Abulkhanov",
"Indraneil Paul",
"Zhuang Li",
"Wen-Ding Li",
"Megan Risdal",
"Jia Li",
"Jian Zhu",
"Terry Yue Zhuo",
"Evgenii Zheltonozhskii",
"Nii Osae Osae Dade",
"Wenhao Yu",
"Lucas Krauß",
"Naman Jain",
"Yixuan Su",
"Xuanli He",
"Manan Dey",
"Edoardo Abati",
"Yekun Chai",
"Niklas Muennighoff",
"Xiangru Tang",
"Muhtasham Oblokulov",
"Christopher Akiki",
"Marc Marone",
"Chenghao Mou",
"Mayank Mishra",
"Alex Gu",
"Binyuan Hui",
"Tri Dao",
"Armel Zebaze",
"Olivier Dehaene",
"Nicolas Patry",
"Canwen Xu",
"Julian McAuley",
"Han Hu",
"Torsten Scholak",
"Sebastien Paquet",
"Jennifer Robinson",
"Carolyn Jane Anderson",
"Nicolas Chapados",
"Mostofa Patwary",
"Nima Tajbakhsh",
"Yacine Jernite",
"Carlos Muñoz Ferrandis",
"Lingming Zhang",
"Sean Hughes",
"Thomas Wolf",
"Arjun Guha",
"Leandro von Werra",
"Harm de Vries"
]
| The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data. |
|
2024-03-01T00:00:00 | 2402.19159 | Trajectory Consistency Distillation | [
"Jianbin Zheng",
"Minghui Hu",
"Zhongyi Fan",
"Chaoyue Wang",
"Changxing Ding",
"Dacheng Tao",
"Tat-Jen Cham"
]
| Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. To address this limitation, we initially delve into and elucidate the underlying causes. Our investigation identifies that the primary issue stems from errors in three distinct areas. Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling. The trajectory consistency function diminishes the distillation errors by broadening the scope of the self-consistency boundary condition and endowing the TCD with the ability to accurately trace the entire trajectory of the Probability Flow ODE. Additionally, strategic stochastic sampling is specifically designed to circumvent the accumulated errors inherent in multi-step consistency sampling, which is meticulously tailored to complement the TCD model. Experiments demonstrate that TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs. |
|
2024-03-01T00:00:00 | 2402.18668 | Simple linear attention language models balance the recall-throughput tradeoff | [
"Simran Arora",
"Sabri Eyuboglu",
"Michael Zhang",
"Aman Timalsina",
"Silas Alberti",
"Dylan Zinsley",
"James Zou",
"Atri Rudra",
"Christopher Ré"
]
| https://github.com/HazyResearch/based | Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of attention-based models is bottle-necked during inference by the KV-cache's aggressive memory consumption. In this work, we explore whether we can improve language model efficiency (e.g. by reducing memory consumption) without compromising on recall. By applying experiments and theory to a broad set of architectures, we identify a key tradeoff between a model's state size and recall ability. We show that efficient alternatives to attention (e.g. H3, Mamba, RWKV) maintain a fixed-size recurrent state, but struggle at recall. We propose BASED a simple architecture combining linear and sliding window attention. By varying BASED window size and linear attention feature dimension, we can dial the state size and traverse the pareto frontier of the recall-memory tradeoff curve, recovering the full quality of attention on one end and the small state size of attention-alternatives on the other. We train language models up to 1.3b parameters and show that BASED matches the strongest sub-quadratic models (e.g. Mamba) in perplexity and outperforms them on real-world recall-intensive tasks by 6.22 accuracy points. Implementations of linear attention are often less efficient than optimized standard attention implementations. To make BASED competitive, we develop IO-aware algorithms that enable 24x higher throughput on language generation than FlashAttention-2, when generating 1024 tokens using 1.3b parameter models. Code for this work is provided at: https://github.com/HazyResearch/based. |
2024-03-01T00:00:00 | 2402.18734 | Priority Sampling of Large Language Models for Compilers | [
"Dejan Grubisic",
"Chris Cummins",
"Volker Seeker",
"Hugh Leather"
]
| Large language models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often produce repeated samples for low temperatures and incoherent samples for high temperatures. Furthermore, the temperature coefficient has to be tuned for each task, limiting its usability. We present Priority Sampling, a simple and deterministic sampling technique that produces unique samples ordered by the model's confidence. Each new sample expands the unexpanded token with the highest probability in the augmented search tree. Additionally, Priority Sampling supports generation based on regular expression that provides a controllable and structured exploration process. Priority Sampling outperforms Nucleus Sampling for any number of samples, boosting the performance of the original model from 2.87% to 5% improvement over -Oz. Moreover, it outperforms the autotuner used for the generation of labels for the training of the original model in just 30 samples. |
|
2024-03-01T00:00:00 | 2402.18796 | MOSAIC: A Modular System for Assistive and Interactive Cooking | [
"Huaxiaoyue Wang",
"Kushal Kedia",
"Juntao Ren",
"Rahma Abdullah",
"Atiksh Bhardwaj",
"Angela Chao",
"Kelly Y Chen",
"Nathaniel Chin",
"Prithwish Dan",
"Xinyi Fan",
"Gonzalo Gonzalez-Pumariega",
"Aditya Kompella",
"Maximus Adrian Pace",
"Yash Sharma",
"Xiangwan Sun",
"Neha Sunkara",
"Sanjiban Choudhury"
]
| We present MOSAIC, a modular architecture for home robots to perform complex collaborative tasks, such as cooking with everyday users. MOSAIC tightly collaborates with humans, interacts with users using natural language, coordinates multiple robots, and manages an open vocabulary of everyday objects. At its core, MOSAIC employs modularity: it leverages multiple large-scale pre-trained models for general tasks like language and image recognition, while using streamlined modules designed for task-specific control. We extensively evaluate MOSAIC on 60 end-to-end trials where two robots collaborate with a human user to cook a combination of 6 recipes. We also extensively test individual modules with 180 episodes of visuomotor picking, 60 episodes of human motion forecasting, and 46 online user evaluations of the task planner. We show that MOSAIC is able to efficiently collaborate with humans by running the overall system end-to-end with a real human user, completing 68.3% (41/60) collaborative cooking trials of 6 different recipes with a subtask completion rate of 91.6%. Finally, we discuss the limitations of the current system and exciting open challenges in this domain. The project's website is at https://portal-cornell.github.io/MOSAIC/ |
|
2024-03-01T00:00:00 | 2402.19481 | DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models | [
"Muyang Li",
"Tianle Cai",
"Jiaxin Cao",
"Qinsheng Zhang",
"Han Cai",
"Junjie Bai",
"Yangqing Jia",
"Ming-Yu Liu",
"Kai Li",
"Song Han"
]
| https://github.com/mit-han-lab/distrifuser | Diffusion models have achieved great success in synthesizing high-quality images. However, generating high-resolution images with diffusion models is still challenging due to the enormous computational costs, resulting in a prohibitive latency for interactive applications. In this paper, we propose DistriFusion to tackle this problem by leveraging parallelism across multiple GPUs. Our method splits the model input into multiple patches and assigns each patch to a GPU. However, na\"{\i}vely implementing such an algorithm breaks the interaction between patches and loses fidelity, while incorporating such an interaction will incur tremendous communication overhead. To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step. Therefore, our method supports asynchronous communication, which can be pipelined by computation. Extensive experiments show that our method can be applied to recent Stable Diffusion XL with no quality degradation and achieve up to a 6.1times speedup on eight NVIDIA A100s compared to one. Our code is publicly available at https://github.com/mit-han-lab/distrifuser. |
2024-03-04T00:00:00 | 2403.00522 | VisionLLaMA: A Unified LLaMA Interface for Vision Tasks | [
"Xiangxiang Chu",
"Jianlin Su",
"Bo Zhang",
"Chunhua Shen"
]
| https://github.com/Meituan-AutoML/VisionLLaMA | Large language models are built on top of a transformer-based architecture to process textual inputs. For example, the LLaMA stands out among many open-source implementations. Can the same transformer be used to process 2D images? In this paper, we answer this question by unveiling a LLaMA-like vision transformer in plain and pyramid forms, termed VisionLLaMA, which is tailored for this purpose. VisionLLaMA is a unified and generic modelling framework for solving most vision tasks. We extensively evaluate its effectiveness using typical pre-training paradigms in a good portion of downstream tasks of image perception and especially image generation. In many cases, VisionLLaMA have exhibited substantial gains over the previous state-of-the-art vision transformers. We believe that VisionLLaMA can serve as a strong new baseline model for vision generation and understanding. Our code will be released at https://github.com/Meituan-AutoML/VisionLLaMA. |
2024-03-04T00:00:00 | 2403.00504 | Learning and Leveraging World Models in Visual Representation Learning | [
"Quentin Garrido",
"Mahmoud Assran",
"Nicolas Ballas",
"Adrien Bardes",
"Laurent Najman",
"Yann LeCun"
]
| Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model. While previously limited to predicting missing parts of an input, we explore how to generalize the JEPA prediction task to a broader set of corruptions. We introduce Image World Models, an approach that goes beyond masked image modeling and learns to predict the effect of global photometric transformations in latent space. We study the recipe of learning performant IWMs and show that it relies on three key aspects: conditioning, prediction difficulty, and capacity. Additionally, we show that the predictive world model learned by IWM can be adapted through finetuning to solve diverse tasks; a fine-tuned IWM world model matches or surpasses the performance of previous self-supervised methods. Finally, we show that learning with an IWM allows one to control the abstraction level of the learned representations, learning invariant representations such as contrastive methods, or equivariant representations such as masked image modelling. |
|
2024-03-04T00:00:00 | 2403.00745 | AtP*: An efficient and scalable method for localizing LLM behaviour to components | [
"János Kramár",
"Tom Lieberum",
"Rohin Shah",
"Neel Nanda"
]
| Activation Patching is a method of directly computing causal attributions of behavior to model components. However, applying it exhaustively requires a sweep with cost scaling linearly in the number of model components, which can be prohibitively expensive for SoTA Large Language Models (LLMs). We investigate Attribution Patching (AtP), a fast gradient-based approximation to Activation Patching and find two classes of failure modes of AtP which lead to significant false negatives. We propose a variant of AtP called AtP*, with two changes to address these failure modes while retaining scalability. We present the first systematic study of AtP and alternative methods for faster activation patching and show that AtP significantly outperforms all other investigated methods, with AtP* providing further significant improvement. Finally, we provide a method to bound the probability of remaining false negatives of AtP* estimates. |
|
2024-03-04T00:00:00 | 2403.00071 | Resonance RoPE: Improving Context Length Generalization of Large Language Models | [
"Suyuchen Wang",
"Ivan Kobyzev",
"Peng Lu",
"Mehdi Rezagholizadeh",
"Bang Liu"
]
| This paper addresses the challenge of train-short-test-long (TSTL) scenarios in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE), where models pre-trained on shorter sequences face difficulty with out-of-distribution (OOD) token positions in longer sequences. We introduce Resonance RoPE, a novel approach designed to narrow the generalization gap in TSTL scenarios by refining the interpolation of RoPE features for OOD positions, significantly improving the model performance without additional online computational costs. Furthermore, we present PosGen, a new synthetic benchmark specifically designed for fine-grained behavior analysis in TSTL scenarios, aiming to isolate the constantly increasing difficulty of token generation on long contexts from the challenges of recognizing new token positions. Our experiments on synthetic tasks show that after applying Resonance RoPE, Transformers recognize OOD position better and more robustly. Our extensive LLM experiments also show superior performance after applying Resonance RoPE to the current state-of-the-art RoPE scaling method, YaRN, on both upstream language modeling tasks and a variety of downstream long-text applications. |
|
2024-03-04T00:00:00 | 2403.00483 | RealCustom: Narrowing Real Text Word for Real-Time Open-Domain Text-to-Image Customization | [
"Mengqi Huang",
"Zhendong Mao",
"Mingcong Liu",
"Qian He",
"Yongdong Zhang"
]
| Text-to-image customization, which aims to synthesize text-driven images for the given subjects, has recently revolutionized content creation. Existing works follow the pseudo-word paradigm, i.e., represent the given subjects as pseudo-words and then compose them with the given text. However, the inherent entangled influence scope of pseudo-words with the given text results in a dual-optimum paradox, i.e., the similarity of the given subjects and the controllability of the given text could not be optimal simultaneously. We present RealCustom that, for the first time, disentangles similarity from controllability by precisely limiting subject influence to relevant parts only, achieved by gradually narrowing real text word from its general connotation to the specific subject and using its cross-attention to distinguish relevance. Specifically, RealCustom introduces a novel "train-inference" decoupled framework: (1) during training, RealCustom learns general alignment between visual conditions to original textual conditions by a novel adaptive scoring module to adaptively modulate influence quantity; (2) during inference, a novel adaptive mask guidance strategy is proposed to iteratively update the influence scope and influence quantity of the given subjects to gradually narrow the generation of the real text word. Comprehensive experiments demonstrate the superior real-time customization ability of RealCustom in the open domain, achieving both unprecedented similarity of the given subjects and controllability of the given text for the first time. The project page is https://corleone-huang.github.io/realcustom/. |
|
2024-03-05T00:00:00 | 2403.01807 | ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models | [
"Lukas Höllein",
"Aljaž Božič",
"Norman Müller",
"David Novotny",
"Hung-Yu Tseng",
"Christian Richardt",
"Michael Zollhöfer",
"Matthias Nießner"
]
| https://github.com/facebookresearch/ViewDiff | 3D asset generation is getting massive amounts of attention, inspired by the recent success of text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on synthetic data, which often results in non-photorealistic 3D objects without backgrounds. In this paper, we present a method that leverages pretrained text-to-image models as a prior, and learn to generate multi-view images in a single denoising process from real-world data. Concretely, we propose to integrate 3D volume-rendering and cross-frame-attention layers into each block of the existing U-Net network of the text-to-image model. Moreover, we design an autoregressive generation that renders more 3D-consistent images at any viewpoint. We train our model on real-world datasets of objects and showcase its capabilities to generate instances with a variety of high-quality shapes and textures in authentic surroundings. Compared to the existing methods, the results generated by our method are consistent, and have favorable visual quality (-30% FID, -37% KID). |
2024-03-05T00:00:00 | 2403.02084 | ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models | [
"Jiaxiang Cheng",
"Pan Xie",
"Xin Xia",
"Jiashi Li",
"Jie Wu",
"Yuxi Ren",
"Huixia Li",
"Xuefeng Xiao",
"Min Zheng",
"Lean Fu"
]
| https://github.com/bytedance/res-adapter | Recent advancement in text-to-image models (e.g., Stable Diffusion) and corresponding personalized technologies (e.g., DreamBooth and LoRA) enables individuals to generate high-quality and imaginative images. However, they often suffer from limitations when generating images with resolutions outside of their trained domain. To overcome this limitation, we present the Resolution Adapter (ResAdapter), a domain-consistent adapter designed for diffusion models to generate images with unrestricted resolutions and aspect ratios. Unlike other multi-resolution generation methods that process images of static resolution with complex post-process operations, ResAdapter directly generates images with the dynamical resolution. Especially, after learning a deep understanding of pure resolution priors, ResAdapter trained on the general dataset, generates resolution-free images with personalized diffusion models while preserving their original style domain. Comprehensive experiments demonstrate that ResAdapter with only 0.5M can process images with flexible resolutions for arbitrary diffusion models. More extended experiments demonstrate that ResAdapter is compatible with other modules (e.g., ControlNet, IP-Adapter and LCM-LoRA) for image generation across a broad range of resolutions, and can be integrated into other multi-resolution model (e.g., ElasticDiffusion) for efficiently generating higher-resolution images. Project link is https://res-adapter.github.io |
2024-03-05T00:00:00 | 2403.01800 | AtomoVideo: High Fidelity Image-to-Video Generation | [
"Litong Gong",
"Yiran Zhu",
"Weijie Li",
"Xiaoyang Kang",
"Biao Wang",
"Tiezheng Ge",
"Bo Zheng"
]
| Recently, video generation has achieved significant rapid development based on superior text-to-image generation techniques. In this work, we propose a high fidelity framework for image-to-video generation, named AtomoVideo. Based on multi-granularity image injection, we achieve higher fidelity of the generated video to the given image. In addition, thanks to high quality datasets and training strategies, we achieve greater motion intensity while maintaining superior temporal consistency and stability. Our architecture extends flexibly to the video frame prediction task, enabling long sequence prediction through iterative generation. Furthermore, due to the design of adapter training, our approach can be well combined with existing personalised models and controllable modules. By quantitatively and qualitatively evaluation, AtomoVideo achieves superior results compared to popular methods, more examples can be found on our project website: https://atomo- video.github.io/. |
|
2024-03-05T00:00:00 | 2403.01422 | MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies | [
"Zhende Song",
"Chenchen Wang",
"Jiamu Sheng",
"Chi Zhang",
"Gang Yu",
"Jiayuan Fan",
"Tao Chen"
]
| https://github.com/Deaddawn/MovieLLM-code | The development of multimodal models has marked a significant step forward in how machines understand videos. These models have shown promise in analyzing short video clips. However, when it comes to longer formats like movies, they often fall short. The main hurdles are the lack of high-quality, diverse video data and the intensive work required to collect or annotate such data. In the face of these challenges, we propose MovieLLM, a novel framework designed to create synthetic, high-quality data for long videos. This framework leverages the power of GPT-4 and text-to-image models to generate detailed scripts and corresponding visuals. Our approach stands out for its flexibility and scalability, making it a superior alternative to traditional data collection methods. Our extensive experiments validate that the data produced by MovieLLM significantly improves the performance of multimodal models in understanding complex video narratives, overcoming the limitations of existing datasets regarding scarcity and bias. |
2024-03-05T00:00:00 | 2403.01779 | OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on | [
"Yuhao Xu",
"Tao Gu",
"Weifeng Chen",
"Chengcai Chen"
]
| https://github.com/levihsu/OOTDiffusion | Image-based virtual try-on (VTON), which aims to generate an outfitted image of a target human wearing an in-shop garment, is a challenging image-synthesis task calling for not only high fidelity of the outfitted human but also full preservation of garment details. To tackle this issue, we propose Outfitting over Try-on Diffusion (OOTDiffusion), leveraging the power of pretrained latent diffusion models and designing a novel network architecture for realistic and controllable virtual try-on. Without an explicit warping process, we propose an outfitting UNet to learn the garment detail features, and merge them with the target human body via our proposed outfitting fusion in the denoising process of diffusion models. In order to further enhance the controllability of our outfitting UNet, we introduce outfitting dropout to the training process, which enables us to adjust the strength of garment features through classifier-free guidance. Our comprehensive experiments on the VITON-HD and Dress Code datasets demonstrate that OOTDiffusion efficiently generates high-quality outfitted images for arbitrary human and garment images, which outperforms other VTON methods in both fidelity and controllability, indicating an impressive breakthrough in virtual try-on. Our source code is available at https://github.com/levihsu/OOTDiffusion. |
2024-03-05T00:00:00 | 2403.01487 | InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding | [
"Haogeng Liu",
"Quanzeng You",
"Xiaotian Han",
"Yiqi Wang",
"Bohan Zhai",
"Yongfei Liu",
"Yunzhe Tao",
"Huaibo Huang",
"Ran He",
"Hongxia Yang"
]
| https://github.com/InfiMM/infimm-hd/ | Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intricate details within high-resolution images. Despite being indispensable for the development of robust MLLMs, this area remains underinvestigated. To tackle this challenge, our work introduces InfiMM-HD, a novel architecture specifically designed for processing images of different resolutions with low computational overhead. This innovation facilitates the enlargement of MLLMs to higher-resolution capabilities. InfiMM-HD incorporates a cross-attention module and visual windows to reduce computation costs. By integrating this architectural design with a four-stage training pipeline, our model attains improved visual perception efficiently and cost-effectively. Empirical study underscores the robustness and effectiveness of InfiMM-HD, opening new avenues for exploration in related areas. Codes and models can be found at https://huggingface.co/Infi-MM/infimm-hd |
2024-03-05T00:00:00 | 2403.02151 | TripoSR: Fast 3D Object Reconstruction from a Single Image | [
"Dmitry Tochilkin",
"David Pankratz",
"Zexiang Liu",
"Zixuan Huang",
"Adam Letts",
"Yangguang Li",
"Ding Liang",
"Christian Laforte",
"Varun Jampani",
"Yan-Pei Cao"
]
| https://github.com/VAST-AI-Research/TripoSR | This technical report introduces TripoSR, a 3D reconstruction model leveraging transformer architecture for fast feed-forward 3D generation, producing 3D mesh from a single image in under 0.5 seconds. Building upon the LRM network architecture, TripoSR integrates substantial improvements in data processing, model design, and training techniques. Evaluations on public datasets show that TripoSR exhibits superior performance, both quantitatively and qualitatively, compared to other open-source alternatives. Released under the MIT license, TripoSR is intended to empower researchers, developers, and creatives with the latest advancements in 3D generative AI. |
2024-03-05T00:00:00 | 2403.01444 | 3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos | [
"Jiakai Sun",
"Han Jiao",
"Guangyuan Li",
"Zhanjie Zhang",
"Lei Zhao",
"Wei Xing"
]
| https://github.com/SJoJoK/3DGStream | Constructing photo-realistic Free-Viewpoint Videos (FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advancements achieved by current neural rendering techniques, these methods generally require complete video sequences for offline training and are not capable of real-time rendering. To address these constraints, we introduce 3DGStream, a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real-time rendering at 200 FPS. Specifically, we utilize 3D Gaussians (3DGs) to represent the scene. Instead of the na\"ive approach of directly optimizing 3DGs per-frame, we employ a compact Neural Transformation Cache (NTC) to model the translations and rotations of 3DGs, markedly reducing the training time and storage required for each FVV frame. Furthermore, we propose an adaptive 3DG addition strategy to handle emerging objects in dynamic scenes. Experiments demonstrate that 3DGStream achieves competitive performance in terms of rendering speed, image quality, training time, and model storage when compared with state-of-the-art methods. |
2024-03-05T00:00:00 | 2403.02338 | Twisting Lids Off with Two Hands | [
"Toru Lin",
"Zhao-Heng Yin",
"Haozhi Qi",
"Pieter Abbeel",
"Jitendra Malik"
]
| Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, attributed to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system. In this work, we consider the problem of twisting lids of various bottle-like objects with two hands, and demonstrate that policies trained in simulation using deep reinforcement learning can be effectively transferred to the real world. With novel engineering insights into physical modeling, real-time perception, and reward design, the policy demonstrates generalization capabilities across a diverse set of unseen objects, showcasing dynamic and dexterous behaviors. Our findings serve as compelling evidence that deep reinforcement learning combined with sim-to-real transfer remains a promising approach for addressing manipulation problems of unprecedented complexity. |
|
2024-03-05T00:00:00 | 2403.01823 | RT-H: Action Hierarchies Using Language | [
"Suneel Belkhale",
"Tianli Ding",
"Ted Xiao",
"Pierre Sermanet",
"Quon Vuong",
"Jonathan Tompson",
"Yevgen Chebotar",
"Debidatta Dwibedi",
"Dorsa Sadigh"
]
| Language provides a way to break down complex concepts into digestible pieces. Recent works in robot imitation learning use language-conditioned policies that predict actions given visual observations and the high-level task specified in language. These methods leverage the structure of natural language to share data between semantically similar tasks (e.g., "pick coke can" and "pick an apple") in multi-task datasets. However, as tasks become more semantically diverse (e.g., "pick coke can" and "pour cup"), sharing data between tasks becomes harder, so learning to map high-level tasks to actions requires much more demonstration data. To bridge tasks and actions, our insight is to teach the robot the language of actions, describing low-level motions with more fine-grained phrases like "move arm forward". Predicting these language motions as an intermediate step between tasks and actions forces the policy to learn the shared structure of low-level motions across seemingly disparate tasks. Furthermore, a policy that is conditioned on language motions can easily be corrected during execution through human-specified language motions. This enables a new paradigm for flexible policies that can learn from human intervention in language. Our method RT-H builds an action hierarchy using language motions: it first learns to predict language motions, and conditioned on this and the high-level task, it predicts actions, using visual context at all stages. We show that RT-H leverages this language-action hierarchy to learn policies that are more robust and flexible by effectively tapping into multi-task datasets. We show that these policies not only allow for responding to language interventions, but can also learn from such interventions and outperform methods that learn from teleoperated interventions. Our website and videos are found at https://rt-hierarchy.github.io. |
|
2024-03-05T00:00:00 | 2403.00818 | DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models | [
"Wei He",
"Kai Han",
"Yehui Tang",
"Chengcheng Wang",
"Yujie Yang",
"Tianyu Guo",
"Yunhe Wang"
]
| https://github.com/WailordHe/DenseSSM | Large language models (LLMs) face a daunting challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture. While state space model (SSM) is a new type of foundational network architecture offering lower computational complexity, their performance has yet to fully rival that of Transformers. This paper introduces DenseSSM, a novel approach to enhance the flow of hidden information between layers in SSMs. By selectively integrating shallowlayer hidden states into deeper layers, DenseSSM retains fine-grained information crucial for the final output. Dense connections enhanced DenseSSM still maintains the training parallelizability and inference efficiency. The proposed method can be widely applicable to various SSM types like RetNet and Mamba. With similar model size, DenseSSM achieves significant improvements, exemplified by DenseRetNet outperforming the original RetNet with up to 5% accuracy improvement on public benchmarks. |
2024-03-06T00:00:00 | 2403.02884 | MathScale: Scaling Instruction Tuning for Mathematical Reasoning | [
"Zhengyang Tang",
"Xingxing Zhang",
"Benyou Wan",
"Furu Wei"
]
| Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data using frontier LLMs (e.g., {\tt GPT-3.5}). Inspired by the cognitive mechanism in human mathematical learning, it first extracts topics and knowledge points from seed math questions and then build a concept graph, which is subsequently used to generate new math questions. MathScale exhibits effective scalability along the size axis of the math dataset that we generate. As a result, we create a mathematical reasoning dataset (MathScaleQA) containing two million math question-answer pairs. To evaluate mathematical reasoning abilities of LLMs comprehensively, we construct {\sc MwpBench}, a benchmark of Math Word Problems, which is a collection of ten datasets (including GSM8K and MATH) covering K-12, college, and competition level math problems. We apply MathScaleQA to fine-tune open-source LLMs (e.g., LLaMA-2 and Mistral), resulting in significantly improved capabilities in mathematical reasoning. Evaluated on {\sc MwpBench}, MathScale-7B achieves state-of-the-art performance across all datasets, surpassing its best peers of equivalent size by 42.9\% in micro average accuracy and 43.7\% in macro average accuracy, respectively. |
|
2024-03-06T00:00:00 | 2403.03206 | Scaling Rectified Flow Transformers for High-Resolution Image Synthesis | [
"Patrick Esser",
"Sumith Kulal",
"Andreas Blattmann",
"Rahim Entezari",
"Jonas Müller",
"Harry Saini",
"Yam Levi",
"Dominik Lorenz",
"Axel Sauer",
"Frederic Boesel",
"Dustin Podell",
"Tim Dockhorn",
"Zion English",
"Kyle Lacey",
"Alex Goodwin",
"Yannik Marek",
"Robin Rombach"
]
| Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension, typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations. Our largest models outperform state-of-the-art models, and we will make our experimental data, code, and model weights publicly available. |
|
2024-03-06T00:00:00 | 2403.03163 | Design2Code: How Far Are We From Automating Front-End Engineering? | [
"Chenglei Si",
"Yanzhe Zhang",
"Zhengyuan Yang",
"Ruibo Liu",
"Diyi Yang"
]
| https://github.com/NoviScl/Design2Code | Generative AI has made rapid advancements in recent years, achieving unprecedented capabilities in multimodal understanding and code generation. This can enable a new paradigm of front-end development, in which multimodal LLMs might directly convert visual designs into code implementations. In this work, we formalize this as a Design2Code task and conduct comprehensive benchmarking. Specifically, we manually curate a benchmark of 484 diverse real-world webpages as test cases and develop a set of automatic evaluation metrics to assess how well current multimodal LLMs can generate the code implementations that directly render into the given reference webpages, given the screenshots as input. We also complement automatic metrics with comprehensive human evaluations. We develop a suite of multimodal prompting methods and show their effectiveness on GPT-4V and Gemini Pro Vision. We further finetune an open-source Design2Code-18B model that successfully matches the performance of Gemini Pro Vision. Both human evaluation and automatic metrics show that GPT-4V performs the best on this task compared to other models. Moreover, annotators think GPT-4V generated webpages can replace the original reference webpages in 49% of cases in terms of visual appearance and content; and perhaps surprisingly, in 64% of cases GPT-4V generated webpages are considered better than the original reference webpages. Our fine-grained break-down metrics indicate that open-source models mostly lag in recalling visual elements from the input webpages and in generating correct layout designs, while aspects like text content and coloring can be drastically improved with proper finetuning. |
2024-03-06T00:00:00 | 2403.02677 | Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters | [
"Weizhi Wang",
"Khalil Mrini",
"Linjie Yang",
"Sateesh Kumar",
"Yu Tian",
"Xifeng Yan",
"Heng Wang"
]
| We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g., CLIPScore) via integrating the recent advances in MLMs. We design four distinct yet complementary metrics to holistically measure the quality of image-text data. A new pipeline is established to construct high-quality instruction data for fine-tuning MLMs as data filters. Comparing with CLIPScore, our MLM filters produce more precise and comprehensive scores that directly improve the quality of filtered data and boost the performance of pre-trained models. We achieve significant improvements over CLIPScore on popular foundation models (i.e., CLIP and BLIP2) and various downstream tasks. Our MLM filter can generalize to different models and tasks, and be used as a drop-in replacement for CLIPScore. An additional ablation study is provided to verify our design choices for the MLM filter. |
|
2024-03-06T00:00:00 | 2403.03003 | Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models | [
"Gen Luo",
"Yiyi Zhou",
"Yuxin Zhang",
"Xiawu Zheng",
"Xiaoshuai Sun",
"Rongrong Ji"
]
| https://github.com/luogen1996/LLaVA-HR | Despite remarkable progress, existing multimodal large language models (MLLMs) are still inferior in granular visual recognition. Contrary to previous works, we study this problem from the perspective of image resolution, and reveal that a combination of low- and high-resolution visual features can effectively mitigate this shortcoming. Based on this observation, we propose a novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation (MRA). In particular, MRA adopts two visual pathways for images with different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the novel mixture-of-resolution adapters (MR-Adapters). This design also greatly reduces the input sequence length of MLLMs. To validate MRA, we apply it to a recent MLLM called LLaVA, and term the new model LLaVA-HR. We conduct extensive experiments on 11 vision-language (VL) tasks, which show that LLaVA-HR outperforms existing MLLMs on 8 VL tasks, e.g., +9.4% on TextVQA. More importantly, both training and inference of LLaVA-HR remain efficient with MRA, e.g., 20 training hours and 3times inference speed than LLaVA-1.5. Source codes are released at: https://github.com/luogen1996/LLaVA-HR. |
2024-03-06T00:00:00 | 2403.02775 | EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs | [
"Hanlin Tang",
"Yifu Sun",
"Decheng Wu",
"Kai Liu",
"Jianchen Zhu",
"Zhanhui Kang"
]
| Large language models (LLMs) have proven to be very superior to conventional methods in various tasks. However, their expensive computations and high memory requirements are prohibitive for deployment. Model quantization is an effective method for reducing this overhead. The problem is that in most previous works, the quantized model was calibrated using few samples from the training data, which might affect the generalization of the quantized LLMs to unknown cases and tasks. Hence in this work, we explore an important question: Can we design a data-independent quantization method for LLMs to guarantee its generalization performance? In this work, we propose EasyQuant, a training-free and data-independent weight-only quantization algorithm for LLMs. Our observation indicates that two factors: outliers in the weight and quantization ranges, are essential for reducing the quantization error. Therefore, in EasyQuant, we leave the outliers (less than 1%) unchanged and optimize the quantization range to reduce the reconstruction error. With these methods, we surprisingly find that EasyQuant achieves comparable performance to the original model. Since EasyQuant does not depend on any training data, the generalization performance of quantized LLMs is safely guaranteed. Moreover, EasyQuant can be implemented in parallel so that the quantized model could be attained in a few minutes even for LLMs over 100B. To our best knowledge, we are the first work that achieves almost lossless quantization performance for LLMs under a data-independent setting and our algorithm runs over 10 times faster than the data-dependent methods. |
|
2024-03-06T00:00:00 | 2403.02545 | Wukong: Towards a Scaling Law for Large-Scale Recommendation | [
"Buyun Zhang",
"Liang Luo",
"Yuxin Chen",
"Jade Nie",
"Xi Liu",
"Daifeng Guo",
"Yanli Zhao",
"Shen Li",
"Yuchen Hao",
"Yantao Yao",
"Guna Lakshminarayanan",
"Ellie Dingqiao Wen",
"Jongsoo Park",
"Maxim Naumov",
"Wenlin Chen"
]
| Scaling laws play an instrumental role in the sustainable improvement in model quality. Unfortunately, recommendation models to date do not exhibit such laws similar to those observed in the domain of large language models, due to the inefficiencies of their upscaling mechanisms. This limitation poses significant challenges in adapting these models to increasingly more complex real-world datasets. In this paper, we propose an effective network architecture based purely on stacked factorization machines, and a synergistic upscaling strategy, collectively dubbed Wukong, to establish a scaling law in the domain of recommendation. Wukong's unique design makes it possible to capture diverse, any-order of interactions simply through taller and wider layers. We conducted extensive evaluations on six public datasets, and our results demonstrate that Wukong consistently outperforms state-of-the-art models quality-wise. Further, we assessed Wukong's scalability on an internal, large-scale dataset. The results show that Wukong retains its superiority in quality over state-of-the-art models, while holding the scaling law across two orders of magnitude in model complexity, extending beyond 100 Gflop or equivalently up to GPT-3/LLaMa-2 scale of total training compute, where prior arts fall short. |
|
2024-03-06T00:00:00 | 2403.02827 | Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation | [
"Weijie Li",
"Litong Gong",
"Yiran Zhu",
"Fanda Fan",
"Biao Wang",
"Tiezheng Ge",
"Bo Zheng"
]
| Image-to-video (I2V) generation tasks always suffer from keeping high fidelity in the open domains. Traditional image animation techniques primarily focus on specific domains such as faces or human poses, making them difficult to generalize to open domains. Several recent I2V frameworks based on diffusion models can generate dynamic content for open domain images but fail to maintain fidelity. We found that two main factors of low fidelity are the loss of image details and the noise prediction biases during the denoising process. To this end, we propose an effective method that can be applied to mainstream video diffusion models. This method achieves high fidelity based on supplementing more precise image information and noise rectification. Specifically, given a specified image, our method first adds noise to the input image latent to keep more details, then denoises the noisy latent with proper rectification to alleviate the noise prediction biases. Our method is tuning-free and plug-and-play. The experimental results demonstrate the effectiveness of our approach in improving the fidelity of generated videos. For more image-to-video generated results, please refer to the project website: https://noise-rectification.github.io. |
|
2024-03-06T00:00:00 | 2403.03100 | NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models | [
"Zeqian Ju",
"Yuancheng Wang",
"Kai Shen",
"Xu Tan",
"Detai Xin",
"Dongchao Yang",
"Yanqing Liu",
"Yichong Leng",
"Kaitao Song",
"Siliang Tang",
"Zhizheng Wu",
"Tao Qin",
"Xiang-Yang Li",
"Wei Ye",
"Shikun Zhang",
"Jiang Bian",
"Lei He",
"Jinyu Li",
"Sheng Zhao"
]
| While recent large-scale text-to-speech (TTS) models have achieved significant progress, they still fall short in speech quality, similarity, and prosody. Considering speech intricately encompasses various attributes (e.g., content, prosody, timbre, and acoustic details) that pose significant challenges for generation, a natural idea is to factorize speech into individual subspaces representing different attributes and generate them individually. Motivated by it, we propose NaturalSpeech 3, a TTS system with novel factorized diffusion models to generate natural speech in a zero-shot way. Specifically, 1) we design a neural codec with factorized vector quantization (FVQ) to disentangle speech waveform into subspaces of content, prosody, timbre, and acoustic details; 2) we propose a factorized diffusion model to generate attributes in each subspace following its corresponding prompt. With this factorization design, NaturalSpeech 3 can effectively and efficiently model the intricate speech with disentangled subspaces in a divide-and-conquer way. Experiments show that NaturalSpeech 3 outperforms the state-of-the-art TTS systems on quality, similarity, prosody, and intelligibility. Furthermore, we achieve better performance by scaling to 1B parameters and 200K hours of training data. |
|
2024-03-06T00:00:00 | 2403.02460 | MagicClay: Sculpting Meshes With Generative Neural Fields | [
"Amir Barda",
"Vladimir G. Kim",
"Noam Aigerman",
"Amit H. Bermano",
"Thibault Groueix"
]
| The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time. |
|
2024-03-06T00:00:00 | 2403.02709 | RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches | [
"Priya Sundaresan",
"Quan Vuong",
"Jiayuan Gu",
"Peng Xu",
"Ted Xiao",
"Sean Kirmani",
"Tianhe Yu",
"Michael Stark",
"Ajinkya Jain",
"Karol Hausman",
"Dorsa Sadigh",
"Jeannette Bohg",
"Stefan Schaal"
]
| Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over-specified. In this work, we propose hand-drawn sketches as a modality for goal specification in visual imitation learning. Sketches are easy for users to provide on the fly like language, but similar to images they can also help a downstream policy to be spatially-aware and even go beyond images to disambiguate task-relevant from task-irrelevant objects. We present RT-Sketch, a goal-conditioned policy for manipulation that takes a hand-drawn sketch of the desired scene as input, and outputs actions. We train RT-Sketch on a dataset of paired trajectories and corresponding synthetically generated goal sketches. We evaluate this approach on six manipulation skills involving tabletop object rearrangements on an articulated countertop. Experimentally we find that RT-Sketch is able to perform on a similar level to image or language-conditioned agents in straightforward settings, while achieving greater robustness when language goals are ambiguous or visual distractors are present. Additionally, we show that RT-Sketch has the capacity to interpret and act upon sketches with varied levels of specificity, ranging from minimal line drawings to detailed, colored drawings. For supplementary material and videos, please refer to our website: http://rt-sketch.github.io. |
|
2024-03-06T00:00:00 | 2403.03194 | MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets | [
"Hossein Aboutalebi",
"Hwanjun Song",
"Yusheng Xie",
"Arshit Gupta",
"Justin Sun",
"Hang Su",
"Igor Shalyminov",
"Nikolaos Pappas",
"Siffi Singh",
"Saab Mansour"
]
| Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs. Previous approaches augment textual dialogues with retrieved images, posing privacy, diversity, and quality constraints. In this work, we introduce Multimodal Augmented Generative Images Dialogues (MAGID), a framework to augment text-only dialogues with diverse and high-quality images. Subsequently, a diffusion model is applied to craft corresponding images, ensuring alignment with the identified text. Finally, MAGID incorporates an innovative feedback loop between an image description generation module (textual LLM) and image quality modules (addressing aesthetics, image-text matching, and safety), that work in tandem to generate high-quality and multi-modal dialogues. We compare MAGID to other SOTA baselines on three dialogue datasets, using automated and human evaluation. Our results show that MAGID is comparable to or better than baselines, with significant improvements in human evaluation, especially against retrieval baselines where the image database is small. |
|
2024-03-06T00:00:00 | 2403.02626 | Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use | [
"Imad Eddine Toubal",
"Aditya Avinash",
"Neil Gordon Alldrin",
"Jan Dlabal",
"Wenlei Zhou",
"Enming Luo",
"Otilia Stretcu",
"Hao Xiong",
"Chun-Ta Lu",
"Howard Zhou",
"Ranjay Krishna",
"Ariel Fuxman",
"Tom Duerig"
]
| From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, developing classifiers for such concepts requires substantial manual effort measured in hours, days, or even months to identify and annotate data needed for training. Even with recently proposed Agile Modeling techniques, which enable rapid bootstrapping of image classifiers, users are still required to spend 30 minutes or more of monotonous, repetitive data labeling just to train a single classifier. Drawing on Fiske's Cognitive Miser theory, we propose a new framework that alleviates manual effort by replacing human labeling with natural language interactions, reducing the total effort required to define a concept by an order of magnitude: from labeling 2,000 images to only 100 plus some natural language interactions. Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points. Most importantly, our framework eliminates the need for crowd-sourced annotations. Moreover, our framework ultimately produces lightweight classification models that are deployable in cost-sensitive scenarios. Across 15 subjective concepts and across 2 public image classification datasets, our trained models outperform traditional Agile Modeling as well as state-of-the-art zero-shot classification models like ALIGN, CLIP, CuPL, and large visual question-answering models like PaLI-X. |
|
2024-03-07T00:00:00 | 2403.03507 | GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection | [
"Jiawei Zhao",
"Zhenyu Zhang",
"Beidi Chen",
"Zhangyang Wang",
"Anima Anandkumar",
"Yuandong Tian"
]
| https://github.com/jiaweizzhao/galore | Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix to the frozen pre-trained weight in each layer, reducing trainable parameters and optimizer states. However, such approaches typically underperform training with full-rank weights in both pre-training and fine-tuning stages since they limit the parameter search to a low-rank subspace and alter the training dynamics, and further, may require full-rank warm start. In this work, we propose Gradient Low-Rank Projection (GaLore), a training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA. Our approach reduces memory usage by up to 65.5% in optimizer states while maintaining both efficiency and performance for pre-training on LLaMA 1B and 7B architectures with C4 dataset with up to 19.7B tokens, and on fine-tuning RoBERTa on GLUE tasks. Our 8-bit GaLore further reduces optimizer memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline. Notably, we demonstrate, for the first time, the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory (e.g., NVIDIA RTX 4090) without model parallel, checkpointing, or offloading strategies. |
2024-03-07T00:00:00 | 2403.03954 | 3D Diffusion Policy | [
"Yanjie Ze",
"Gu Zhang",
"Kangning Zhang",
"Chenyuan Hu",
"Muhan Wang",
"Huazhe Xu"
]
| Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. The core design of DP3 is the utilization of a compact 3D visual representation, extracted from sparse point clouds with an efficient point encoder. In our experiments involving 72 simulation tasks, DP3 successfully handles most tasks with just 10 demonstrations and surpasses baselines with a 55.3% relative improvement. In 4 real robot tasks, DP3 demonstrates precise control with a high success rate of 85%, given only 40 demonstrations of each task, and shows excellent generalization abilities in diverse aspects, including space, viewpoint, appearance, and instance. Interestingly, in real robot experiments, DP3 rarely violates safety requirements, in contrast to baseline methods which frequently do, necessitating human intervention. Our extensive evaluation highlights the critical importance of 3D representations in real-world robot learning. Videos, code, and data are available on https://3d-diffusion-policy.github.io . |
|
2024-03-07T00:00:00 | 2403.03883 | SaulLM-7B: A pioneering Large Language Model for Law | [
"Pierre Colombo",
"Telmo Pessoa Pires",
"Malik Boudiaf",
"Dominic Culver",
"Rui Melo",
"Caio Corro",
"Andre F. T. Martins",
"Fabrizio Esposito",
"Vera Lúcia Raposo",
"Sofia Morgado",
"Michael Desa"
]
| In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation. Leveraging the Mistral 7B architecture as its foundation, SaulLM-7B is trained on an English legal corpus of over 30 billion tokens. SaulLM-7B exhibits state-of-the-art proficiency in understanding and processing legal documents. Additionally, we present a novel instructional fine-tuning method that leverages legal datasets to further enhance SaulLM-7B's performance in legal tasks. SaulLM-7B is released under the CC-BY-SA-4.0 License. |
|
2024-03-07T00:00:00 | 2403.03870 | Learning to Decode Collaboratively with Multiple Language Models | [
"Shannon Zejiang Shen",
"Hunter Lang",
"Bailin Wang",
"Yoon Kim",
"David Sontag"
]
| https://github.com/clinicalml/co-llm | We propose a method to teach multiple large language models (LLM) to collaborate by interleaving their generations at the token level. We model the decision of which LLM generates the next token as a latent variable. By optimizing the marginal likelihood of a training set under our latent variable model, the base LLM automatically learns when to generate itself and when to call on one of the ``assistant'' language models to generate, all without direct supervision. Token-level collaboration during decoding allows for a fusion of each model's expertise in a manner tailored to the specific task at hand. Our collaborative decoding is especially useful in cross-domain settings where a generalist base LLM learns to invoke domain expert models. On instruction-following, domain-specific QA, and reasoning tasks, we show that the performance of the joint system exceeds that of the individual models. Through qualitative analysis of the learned latent decisions, we show models trained with our method exhibit several interesting collaboration patterns, e.g., template-filling. Our code is available at https://github.com/clinicalml/co-llm. |
2024-03-07T00:00:00 | 2403.03853 | ShortGPT: Layers in Large Language Models are More Redundant Than You Expect | [
"Xin Men",
"Mingyu Xu",
"Qingyu Zhang",
"Bingning Wang",
"Hongyu Lin",
"Yaojie Lu",
"Xianpei Han",
"Weipeng Chen"
]
| As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters. However, in this study, we discovered that many layers of LLMs exhibit high similarity, and some layers play a negligible role in network functionality. Based on this observation, we define a metric called Block Influence (BI) to gauge the significance of each layer in LLMs. We then propose a straightforward pruning approach: layer removal, in which we directly delete the redundant layers in LLMs based on their BI scores. Experiments demonstrate that our method, which we call ShortGPT, significantly outperforms previous state-of-the-art (SOTA) methods in model pruning. Moreover, ShortGPT is orthogonal to quantization-like methods, enabling further reduction in parameters and computation. The ability to achieve better results through simple layer removal, as opposed to more complex pruning techniques, suggests a high degree of redundancy in the model architecture. |
|
2024-03-07T00:00:00 | 2403.03346 | Enhancing Vision-Language Pre-training with Rich Supervisions | [
"Yuan Gao",
"Kunyu Shi",
"Pengkai Zhu",
"Edouard Belval",
"Oren Nuriel",
"Srikar Appalaraju",
"Shabnam Ghadar",
"Vijay Mahadevan",
"Zhuowen Tu",
"Stefano Soatto"
]
| We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering. Using web screenshots unlocks a treasure trove of visual and textual cues that are not present in using image-text pairs. In S4, we leverage the inherent tree-structured hierarchy of HTML elements and the spatial localization to carefully design 10 pre-training tasks with large scale annotated data. These tasks resemble downstream tasks across different domains and the annotations are cheap to obtain. We demonstrate that, compared to current screenshot pre-training objectives, our innovative pre-training method significantly enhances performance of image-to-text model in nine varied and popular downstream tasks - up to 76.1% improvements on Table Detection, and at least 1% on Widget Captioning. |
|
2024-03-07T00:00:00 | 2403.03956 | Backtracing: Retrieving the Cause of the Query | [
"Rose E. Wang",
"Pawan Wirawarn",
"Omar Khattab",
"Noah Goodman",
"Dorottya Demszky"
]
| https://github.com/rosewang2008/backtracing | Many online content portals allow users to ask questions to supplement their understanding (e.g., of lectures). While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators -- such as lecturers who want to improve their content -- identify segments that _caused_ a user to ask those questions. We introduce the task of backtracing, in which systems retrieve the text segment that most likely caused a user query. We formalize three real-world domains for which backtracing is important in improving content delivery and communication: understanding the cause of (a) student confusion in the Lecture domain, (b) reader curiosity in the News Article domain, and (c) user emotion in the Conversation domain. We evaluate the zero-shot performance of popular information retrieval methods and language modeling methods, including bi-encoder, re-ranking and likelihood-based methods and ChatGPT. While traditional IR systems retrieve semantically relevant information (e.g., details on "projection matrices" for a query "does projecting multiple times still lead to the same point?"), they often miss the causally relevant context (e.g., the lecturer states "projecting twice gets me the same answer as one projection"). Our results show that there is room for improvement on backtracing and it requires new retrieval approaches. We hope our benchmark serves to improve future retrieval systems for backtracing, spawning systems that refine content generation and identify linguistic triggers influencing user queries. Our code and data are open-sourced: https://github.com/rosewang2008/backtracing. |
2024-03-07T00:00:00 | 2403.03950 | Stop Regressing: Training Value Functions via Classification for Scalable Deep RL | [
"Jesse Farebrother",
"Jordi Orbay",
"Quan Vuong",
"Adrien Ali Taïga",
"Yevgen Chebotar",
"Ted Xiao",
"Alex Irpan",
"Sergey Levine",
"Pablo Samuel Castro",
"Aleksandra Faust",
"Aviral Kumar",
"Rishabh Agarwal"
]
| Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression objective to match bootstrapped target values. However, scaling value-based RL methods that use regression to large networks, such as high-capacity Transformers, has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that the benefits of categorical cross-entropy primarily stem from its ability to mitigate issues inherent to value-based RL, such as noisy targets and non-stationarity. Overall, we argue that a simple shift to training value functions with categorical cross-entropy can yield substantial improvements in the scalability of deep RL at little-to-no cost. |
|
2024-03-07T00:00:00 | 2403.03234 | Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling | [
"Yair Schiff",
"Chia-Hsiang Kao",
"Aaron Gokaslan",
"Tri Dao",
"Albert Gu",
"Volodymyr Kuleshov"
]
| Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics. However, modeling genomic sequences introduces challenges such as the need to model long-range token interactions, the effects of upstream and downstream regions of the genome, and the reverse complementarity (RC) of DNA. Here, we propose an architecture motivated by these challenges that builds off the long-range Mamba block, and extends it to a BiMamba component that supports bi-directionality, and to a MambaDNA block that additionally supports RC equivariance. We use MambaDNA as the basis of Caduceus, the first family of RC equivariant bi-directional long-range DNA language models, and we introduce pre-training and fine-tuning strategies that yield Caduceus DNA foundation models. Caduceus outperforms previous long-range models on downstream benchmarks; on a challenging long-range variant effect prediction task, Caduceus exceeds the performance of 10x larger models that do not leverage bi-directionality or equivariance. |
|
2024-03-08T00:00:00 | 2403.04652 | Yi: Open Foundation Models by 01.AI | [
"01. AI",
"Alex Young",
"Bei Chen",
"Chao Li",
"Chengen Huang",
"Ge Zhang",
"Guanwei Zhang",
"Heng Li",
"Jiangcheng Zhu",
"Jianqun Chen",
"Jing Chang",
"Kaidong Yu",
"Peng Liu",
"Qiang Liu",
"Shawn Yue",
"Senbin Yang",
"Shiming Yang",
"Tao Yu",
"Wen Xie",
"Wenhao Huang",
"Xiaohui Hu",
"Xiaoyi Ren",
"Xinyao Niu",
"Pengcheng Nie",
"Yuchi Xu",
"Yudong Liu",
"Yue Wang",
"Yuxuan Cai",
"Zhenyu Gu",
"Zhiyuan Liu",
"Zonghong Dai"
]
| We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models. Our base models achieve strong performance on a wide range of benchmarks like MMLU, and our finetuned chat models deliver strong human preference rate on major evaluation platforms like AlpacaEval and Chatbot Arena. Building upon our scalable super-computing infrastructure and the classical transformer architecture, we attribute the performance of Yi models primarily to its data quality resulting from our data-engineering efforts. For pretraining, we construct 3.1 trillion tokens of English and Chinese corpora using a cascaded data deduplication and quality filtering pipeline. For finetuning, we polish a small scale (less than 10K) instruction dataset over multiple iterations such that every single instance has been verified directly by our machine learning engineers. For vision-language, we combine the chat language model with a vision transformer encoder and train the model to align visual representations to the semantic space of the language model. We further extend the context length to 200K through lightweight continual pretraining and demonstrate strong needle-in-a-haystack retrieval performance. We show that extending the depth of the pretrained checkpoint through continual pretraining further improves performance. We believe that given our current results, continuing to scale up model parameters using thoroughly optimized data will lead to even stronger frontier models. |
|
2024-03-08T00:00:00 | 2403.04437 | StableDrag: Stable Dragging for Point-based Image Editing | [
"Yutao Cui",
"Xiaotong Zhao",
"Guozhen Zhang",
"Shengming Cao",
"Kai Ma",
"Limin Wang"
]
| Point-based image editing has attracted remarkable attention since the emergence of DragGAN. Recently, DragDiffusion further pushes forward the generative quality via adapting this dragging technique to diffusion models. Despite these great success, this dragging scheme exhibits two major drawbacks, namely inaccurate point tracking and incomplete motion supervision, which may result in unsatisfactory dragging outcomes. To tackle these issues, we build a stable and precise drag-based editing framework, coined as StableDrag, by designing a discirminative point tracking method and a confidence-based latent enhancement strategy for motion supervision. The former allows us to precisely locate the updated handle points, thereby boosting the stability of long-range manipulation, while the latter is responsible for guaranteeing the optimized latent as high-quality as possible across all the manipulation steps. Thanks to these unique designs, we instantiate two types of image editing models including StableDrag-GAN and StableDrag-Diff, which attains more stable dragging performance, through extensive qualitative experiments and quantitative assessment on DragBench. |
|
2024-03-08T00:00:00 | 2403.04132 | Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference | [
"Wei-Lin Chiang",
"Lianmin Zheng",
"Ying Sheng",
"Anastasios Nikolas Angelopoulos",
"Tianle Li",
"Dacheng Li",
"Hao Zhang",
"Banghua Zhu",
"Michael Jordan",
"Joseph E. Gonzalez",
"Ion Stoica"
]
| Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwise comparison approach and leverages input from a diverse user base through crowdsourcing. The platform has been operational for several months, amassing over 240K votes. This paper describes the platform, analyzes the data we have collected so far, and explains the tried-and-true statistical methods we are using for efficient and accurate evaluation and ranking of models. We confirm that the crowdsourced questions are sufficiently diverse and discriminating and that the crowdsourced human votes are in good agreement with those of expert raters. These analyses collectively establish a robust foundation for the credibility of Chatbot Arena. Because of its unique value and openness, Chatbot Arena has emerged as one of the most referenced LLM leaderboards, widely cited by leading LLM developers and companies. Our demo is publicly available at https://chat.lmsys.org. |
|
2024-03-08T00:00:00 | 2403.04642 | Teaching Large Language Models to Reason with Reinforcement Learning | [
"Alex Havrilla",
"Yuqing Du",
"Sharath Chandra Raparthy",
"Christoforos Nalmpantis",
"Jane Dwivedi-Yu",
"Maksym Zhuravinskyi",
"Eric Hambro",
"Sainbayar Sukhbaatar",
"Roberta Raileanu"
]
| Reinforcement Learning from Human Feedback (RLHF) has emerged as a dominant approach for aligning LLM outputs with human preferences. Inspired by the success of RLHF, we study the performance of multiple algorithms that learn from feedback (Expert Iteration, Proximal Policy Optimization (PPO), Return-Conditioned RL) on improving LLM reasoning capabilities. We investigate both sparse and dense rewards provided to the LLM both heuristically and via a learned reward model. We additionally start from multiple model sizes and initializations both with and without supervised fine-tuning (SFT) data. Overall, we find all algorithms perform comparably, with Expert Iteration performing best in most cases. Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of 10^6 samples to converge from a pretrained checkpoint. We investigate why this is the case, concluding that during RL training models fail to explore significantly beyond solutions already produced by SFT models. Additionally, we discuss a trade off between maj@1 and pass@96 metric performance during SFT training and how conversely RL training improves both simultaneously. We then conclude by discussing the implications of our findings for RLHF and the future role of RL in LLM fine-tuning. |
|
2024-03-08T00:00:00 | 2403.04706 | Common 7B Language Models Already Possess Strong Math Capabilities | [
"Chen Li",
"Weiqi Wang",
"Jingcheng Hu",
"Yixuan Wei",
"Nanning Zheng",
"Han Hu",
"Zheng Zhang",
"Houwen Peng"
]
| Mathematical capabilities were previously believed to emerge in common language models only at a very large scale or require extensive math-related pre-training. This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities, as evidenced by its impressive accuracy of 97.7% and 72.0% on the GSM8K and MATH benchmarks, respectively, when selecting the best response from 256 random generations. The primary issue with the current base model is the difficulty in consistently eliciting its inherent mathematical capabilities. Notably, the accuracy for the first answer drops to 49.5% and 7.9% on the GSM8K and MATH benchmarks, respectively. We find that simply scaling up the SFT data can significantly enhance the reliability of generating correct answers. However, the potential for extensive scaling is constrained by the scarcity of publicly available math questions. To overcome this limitation, we employ synthetic data, which proves to be nearly as effective as real data and shows no clear saturation when scaled up to approximately one million samples. This straightforward approach achieves an accuracy of 82.6% on GSM8K and 40.6% on MATH using LLaMA-2 7B models, surpassing previous models by 14.2% and 20.8%, respectively. We also provide insights into scaling behaviors across different reasoning complexities and error types. |
|
2024-03-08T00:00:00 | 2403.04746 | LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error | [
"Boshi Wang",
"Hao Fang",
"Jason Eisner",
"Benjamin Van Durme",
"Yu Su"
]
| Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools. However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained. We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned for tool use, only reach a correctness rate in the range of 30% to 60%, far from reliable use in practice. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE), that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. Specifically, STE leverages an LLM's 'imagination' to simulate plausible scenarios for using a tool, after which the LLM interacts with the tool to learn from its execution feedback. Both short-term and long-term memory are employed to improve the depth and breadth of the exploration, respectively. Comprehensive experiments on ToolBench show that STE substantially improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and enabling it to outperform GPT-4. We also show effective continual learning of tools via a simple experience replay strategy. |
|
2024-03-08T00:00:00 | 2403.04732 | How Far Are We from Intelligent Visual Deductive Reasoning? | [
"Yizhe Zhang",
"He Bai",
"Ruixiang Zhang",
"Jiatao Gu",
"Shuangfei Zhai",
"Josh Susskind",
"Navdeep Jaitly"
]
| Vision-Language Models (VLMs) such as GPT-4V have recently demonstrated incredible strides on diverse vision language tasks. We dig into vision-based deductive reasoning, a more sophisticated but less explored realm, and find previously unexposed blindspots in the current SOTA VLMs. Specifically, we leverage Raven's Progressive Matrices (RPMs), to assess VLMs' abilities to perform multi-hop relational and deductive reasoning relying solely on visual clues. We perform comprehensive evaluations of several popular VLMs employing standard strategies such as in-context learning, self-consistency, and Chain-of-thoughts (CoT) on three diverse datasets, including the Mensa IQ test, IntelligenceTest, and RAVEN. The results reveal that despite the impressive capabilities of LLMs in text-based reasoning, we are still far from achieving comparable proficiency in visual deductive reasoning. We found that certain standard strategies that are effective when applied to LLMs do not seamlessly translate to the challenges presented by visual reasoning tasks. Moreover, a detailed analysis reveals that VLMs struggle to solve these tasks mainly because they are unable to perceive and comprehend multiple, confounding abstract patterns in RPM examples. |
|
2024-03-08T00:00:00 | 2403.04692 | PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation | [
"Junsong Chen",
"Chongjian Ge",
"Enze Xie",
"Yue Wu",
"Lewei Yao",
"Xiaozhe Ren",
"Zhongdao Wang",
"Ping Luo",
"Huchuan Lu",
"Zhenguo Li"
]
| https://github.com/PixArt-alpha/PixArt-sigma | In this paper, we introduce PixArt-\Sigma, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution. PixArt-\Sigma represents a significant advancement over its predecessor, PixArt-\alpha, offering images of markedly higher fidelity and improved alignment with text prompts. A key feature of PixArt-\Sigma is its training efficiency. Leveraging the foundational pre-training of PixArt-\alpha, it evolves from the `weaker' baseline to a `stronger' model via incorporating higher quality data, a process we term "weak-to-strong training". The advancements in PixArt-\Sigma are twofold: (1) High-Quality Training Data: PixArt-\Sigma incorporates superior-quality image data, paired with more precise and detailed image captions. (2) Efficient Token Compression: we propose a novel attention module within the DiT framework that compresses both keys and values, significantly improving efficiency and facilitating ultra-high-resolution image generation. Thanks to these improvements, PixArt-\Sigma achieves superior image quality and user prompt adherence capabilities with significantly smaller model size (0.6B parameters) than existing text-to-image diffusion models, such as SDXL (2.6B parameters) and SD Cascade (5.1B parameters). Moreover, PixArt-\Sigma's capability to generate 4K images supports the creation of high-resolution posters and wallpapers, efficiently bolstering the production of high-quality visual content in industries such as film and gaming. |
2024-03-08T00:00:00 | 2403.04116 | Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis | [
"Yuanhao Cai",
"Yixun Liang",
"Jiahao Wang",
"Angtian Wang",
"Yulun Zhang",
"Xiaokang Yang",
"Zongwei Zhou",
"Alan Yuille"
]
| https://github.com/caiyuanhao1998/X-Gaussian | X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on NeRF suffer from long training time and slow inference speed. In this paper, we propose a 3D Gaussian splatting-based framework, namely X-Gaussian, for X-ray novel view synthesis. Firstly, we redesign a radiative Gaussian point cloud model inspired by the isotropic nature of X-ray imaging. Our model excludes the influence of view direction when learning to predict the radiation intensity of 3D points. Based on this model, we develop a Differentiable Radiative Rasterization (DRR) with CUDA implementation. Secondly, we customize an Angle-pose Cuboid Uniform Initialization (ACUI) strategy that directly uses the parameters of the X-ray scanner to compute the camera information and then uniformly samples point positions within a cuboid enclosing the scanned object. Experiments show that our X-Gaussian outperforms state-of-the-art methods by 6.5 dB while enjoying less than 15% training time and over 73x inference speed. The application on sparse-view CT reconstruction also reveals the practical values of our method. Code and models will be publicly available at https://github.com/caiyuanhao1998/X-Gaussian . A video demo of the training process visualization is at https://www.youtube.com/watch?v=gDVf_Ngeghg . |
2024-03-08T00:00:00 | 2403.04634 | Pix2Gif: Motion-Guided Diffusion for GIF Generation | [
"Hitesh Kandala",
"Jianfeng Gao",
"Jianwei Yang"
]
| https://github.com/hiteshK03/Pix2Gif | We present Pix2Gif, a motion-guided diffusion model for image-to-GIF (video) generation. We tackle this problem differently by formulating the task as an image translation problem steered by text and motion magnitude prompts, as shown in teaser fig. To ensure that the model adheres to motion guidance, we propose a new motion-guided warping module to spatially transform the features of the source image conditioned on the two types of prompts. Furthermore, we introduce a perceptual loss to ensure the transformed feature map remains within the same space as the target image, ensuring content consistency and coherence. In preparation for the model training, we meticulously curated data by extracting coherent image frames from the TGIF video-caption dataset, which provides rich information about the temporal changes of subjects. After pretraining, we apply our model in a zero-shot manner to a number of video datasets. Extensive qualitative and quantitative experiments demonstrate the effectiveness of our model -- it not only captures the semantic prompt from text but also the spatial ones from motion guidance. We train all our models using a single node of 16xV100 GPUs. Code, dataset and models are made public at: https://hiteshk03.github.io/Pix2Gif/. |
2024-03-11T00:00:00 | 2403.05438 | VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models | [
"Yabo Zhang",
"Yuxiang Wei",
"Xianhui Lin",
"Zheng Hui",
"Peiran Ren",
"Xuansong Xie",
"Xiangyang Ji",
"Wangmeng Zuo"
]
| https://github.com/YBYBZhang/VideoElevator | Text-to-image diffusion models (T2I) have demonstrated unprecedented capabilities in creating realistic and aesthetic images. On the contrary, text-to-video diffusion models (T2V) still lag far behind in frame quality and text alignment, owing to insufficient quality and quantity of training videos. In this paper, we introduce VideoElevator, a training-free and plug-and-play method, which elevates the performance of T2V using superior capabilities of T2I. Different from conventional T2V sampling (i.e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating. Specifically, temporal motion refining uses encapsulated T2V to enhance temporal consistency, followed by inverting to the noise distribution required by T2I. Then, spatial quality elevating harnesses inflated T2I to directly predict less noisy latent, adding more photo-realistic details. We have conducted experiments in extensive prompts under the combination of various T2V and T2I. The results show that VideoElevator not only improves the performance of T2V baselines with foundational T2I, but also facilitates stylistic video synthesis with personalized T2I. Our code is available at https://github.com/YBYBZhang/VideoElevator. |
2024-03-11T00:00:00 | 2403.05135 | ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment | [
"Xiwei Hu",
"Rui Wang",
"Yixiao Fang",
"Bin Fu",
"Pei Cheng",
"Gang Yu"
]
| https://github.com/ELLA-Diffusion/ELLA | Diffusion models have demonstrated remarkable performance in the domain of text-to-image generation. However, most widely used models still employ CLIP as their text encoder, which constrains their ability to comprehend dense prompts, encompassing multiple objects, detailed attributes, complex relationships, long-text alignment, etc. In this paper, we introduce an Efficient Large Language Model Adapter, termed ELLA, which equips text-to-image diffusion models with powerful Large Language Models (LLM) to enhance text alignment without training of either U-Net or LLM. To seamlessly bridge two pre-trained models, we investigate a range of semantic alignment connector designs and propose a novel module, the Timestep-Aware Semantic Connector (TSC), which dynamically extracts timestep-dependent conditions from LLM. Our approach adapts semantic features at different stages of the denoising process, assisting diffusion models in interpreting lengthy and intricate prompts over sampling timesteps. Additionally, ELLA can be readily incorporated with community models and tools to improve their prompt-following capabilities. To assess text-to-image models in dense prompt following, we introduce Dense Prompt Graph Benchmark (DPG-Bench), a challenging benchmark consisting of 1K dense prompts. Extensive experiments demonstrate the superiority of ELLA in dense prompt following compared to state-of-the-art methods, particularly in multiple object compositions involving diverse attributes and relationships. |
2024-03-11T00:00:00 | 2403.05525 | DeepSeek-VL: Towards Real-World Vision-Language Understanding | [
"Haoyu Lu",
"Wen Liu",
"Bo Zhang",
"Bingxuan Wang",
"Kai Dong",
"Bo Liu",
"Jingxiang Sun",
"Tongzheng Ren",
"Zhuoshu Li",
"Yaofeng Sun",
"Chengqi Deng",
"Hanwei Xu",
"Zhenda Xie",
"Chong Ruan"
]
| https://github.com/deepseek-ai/DeepSeek-VL | We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model. |
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