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2024-01-05T00:00:00 | 2401.02412 | LLM Augmented LLMs: Expanding Capabilities through Composition | [
"Rachit Bansal",
"Bidisha Samanta",
"Siddharth Dalmia",
"Nitish Gupta",
"Shikhar Vashishth",
"Sriram Ganapathy",
"Abhishek Bapna",
"Prateek Jain",
"Partha Talukdar"
]
| Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains. However, due to their monolithic structure, it is challenging and expensive to augment them or impart new skills. On the other hand, due to their adaptation abilities, several new instances of these models are being trained towards new domains and tasks. In this work, we study the problem of efficient and practical composition of existing foundation models with more specific models to enable newer capabilities. To this end, we propose CALM -- Composition to Augment Language Models -- which introduces cross-attention between models to compose their representations and enable new capabilities. Salient features of CALM are: (i) Scales up LLMs on new tasks by 're-using' existing LLMs along with a few additional parameters and data, (ii) Existing model weights are kept intact, and hence preserves existing capabilities, and (iii) Applies to diverse domains and settings. We illustrate that augmenting PaLM2-S with a smaller model trained on low-resource languages results in an absolute improvement of up to 13\% on tasks like translation into English and arithmetic reasoning for low-resource languages. Similarly, when PaLM2-S is augmented with a code-specific model, we see a relative improvement of 40\% over the base model for code generation and explanation tasks -- on-par with fully fine-tuned counterparts. |
|
2024-01-05T00:00:00 | 2401.02038 | Understanding LLMs: A Comprehensive Overview from Training to Inference | [
"Yiheng Liu",
"Hao He",
"Tianle Han",
"Xu Zhang",
"Mengyuan Liu",
"Jiaming Tian",
"Yutong Zhang",
"Jiaqi Wang",
"Xiaohui Gao",
"Tianyang Zhong",
"Yi Pan",
"Shaochen Xu",
"Zihao Wu",
"Zhengliang Liu",
"Xin Zhang",
"Shu Zhang",
"Xintao Hu",
"Tuo Zhang",
"Ning Qiang",
"Tianming Liu",
"Bao Ge"
]
| The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development. |
|
2024-01-05T00:00:00 | 2401.02416 | ODIN: A Single Model for 2D and 3D Perception | [
"Ayush Jain",
"Pushkal Katara",
"Nikolaos Gkanatsios",
"Adam W. Harley",
"Gabriel Sarch",
"Kriti Aggarwal",
"Vishrav Chaudhary",
"Katerina Fragkiadaki"
]
| https://github.com/ayushjain1144/odin | State-of-the-art models on contemporary 3D perception benchmarks like ScanNet consume and label dataset-provided 3D point clouds, obtained through post processing of sensed multiview RGB-D images. They are typically trained in-domain, forego large-scale 2D pre-training and outperform alternatives that featurize the posed RGB-D multiview images instead. The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures. In this paper, we challenge this view and propose ODIN (Omni-Dimensional INstance segmentation), a model that can segment and label both 2D RGB images and 3D point clouds, using a transformer architecture that alternates between 2D within-view and 3D cross-view information fusion. Our model differentiates 2D and 3D feature operations through the positional encodings of the tokens involved, which capture pixel coordinates for 2D patch tokens and 3D coordinates for 3D feature tokens. ODIN achieves state-of-the-art performance on ScanNet200, Matterport3D and AI2THOR 3D instance segmentation benchmarks, and competitive performance on ScanNet, S3DIS and COCO. It outperforms all previous works by a wide margin when the sensed 3D point cloud is used in place of the point cloud sampled from 3D mesh. When used as the 3D perception engine in an instructable embodied agent architecture, it sets a new state-of-the-art on the TEACh action-from-dialogue benchmark. Our code and checkpoints can be found at the project website: https://odin-seg.github.io. |
2024-01-05T00:00:00 | 2401.02072 | ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers | [
"Chen Zheng",
"Ke Sun",
"Da Tang",
"Yukun Ma",
"Yuyu Zhang",
"Chenguang Xi",
"Xun Zhou"
]
| The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specialized areas, and exhibiting a decrease in general capabilities when fine-tuned, particularly analysis ability in small sized models. To address these gaps, we introduce ICE-GRT, utilizing Reinforcement Learning from Human Feedback (RLHF) grounded in Proximal Policy Optimization (PPO), demonstrating remarkable ability in in-domain scenarios without compromising general task performance. Our exploration of ICE-GRT highlights its understanding and reasoning ability to not only generate robust answers but also to provide detailed analyses of the reasons behind the answer. This capability marks a significant progression beyond the scope of Supervised Fine-Tuning models. The success of ICE-GRT is dependent on several crucial factors, including Appropriate Data, Reward Size Scaling, KL-Control, Advantage Normalization, etc. The ICE-GRT model exhibits state-of-the-art performance in domain-specific tasks and across 12 general Language tasks against equivalent size and even larger size LLMs, highlighting the effectiveness of our approach. We provide a comprehensive analysis of the ICE-GRT, underscoring the significant advancements it brings to the field of LLM. |
|
2024-01-05T00:00:00 | 2401.02411 | What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs | [
"Alex Trevithick",
"Matthew Chan",
"Towaki Takikawa",
"Umar Iqbal",
"Shalini De Mello",
"Manmohan Chandraker",
"Ravi Ramamoorthi",
"Koki Nagano"
]
| 3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet, the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution, which sacrifices multiview consistency and the quality of resolved geometry. Consequently, 3D GANs have not yet been able to fully resolve the rich 3D geometry present in 2D images. In this work, we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly "render every pixel" of the full-resolution image during training and inference without post-processing superresolution in 2D. Together with our strategy to learn high-quality surface geometry, our method synthesizes high-resolution 3D geometry and strictly view-consistent images while maintaining image quality on par with baselines relying on post-processing super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ and AFHQ, setting a new standard for unsupervised learning of 3D shapes in 3D GANs. |
|
2024-01-05T00:00:00 | 2401.02385 | TinyLlama: An Open-Source Small Language Model | [
"Peiyuan Zhang",
"Guangtao Zeng",
"Tianduo Wang",
"Wei Lu"
]
| https://github.com/jzhang38/TinyLlama | We present TinyLlama, a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs. Building on the architecture and tokenizer of Llama 2, TinyLlama leverages various advances contributed by the open-source community (e.g., FlashAttention), achieving better computational efficiency. Despite its relatively small size, TinyLlama demonstrates remarkable performance in a series of downstream tasks. It significantly outperforms existing open-source language models with comparable sizes. Our model checkpoints and code are publicly available on GitHub at https://github.com/jzhang38/TinyLlama. |
2024-01-05T00:00:00 | 2401.02415 | LLaMA Pro: Progressive LLaMA with Block Expansion | [
"Chengyue Wu",
"Yukang Gan",
"Yixiao Ge",
"Zeyu Lu",
"Jiahao Wang",
"Ye Feng",
"Ping Luo",
"Ying Shan"
]
| https://github.com/TencentARC/LLaMA-Pro | Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propose a new post-pretraining method for LLMs with an expansion of Transformer blocks. We tune the expanded blocks using only new corpus, efficiently and effectively improving the model's knowledge without catastrophic forgetting. In this paper, we experiment on the corpus of code and math, yielding LLaMA Pro-8.3B, a versatile foundation model initialized from LLaMA2-7B, excelling in general tasks, programming, and mathematics. LLaMA Pro and its instruction-following counterpart (LLaMA Pro-Instruct) achieve advanced performance among various benchmarks, demonstrating superiority over existing open models in the LLaMA family and the immense potential of reasoning and addressing diverse tasks as an intelligent agent. Our findings provide valuable insights into integrating natural and programming languages, laying a solid foundation for developing advanced language agents that operate effectively in various environments. |
2024-01-05T00:00:00 | 2401.02400 | Learning the 3D Fauna of the Web | [
"Zizhang Li",
"Dor Litvak",
"Ruining Li",
"Yunzhi Zhang",
"Tomas Jakab",
"Christian Rupprecht",
"Shangzhe Wu",
"Andrea Vedaldi",
"Jiajun Wu"
]
| Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data, which we overcome by simply learning from 2D Internet images. We show that prior category-specific attempts fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (SBSM), which automatically discovers a small set of base animal shapes by combining geometric inductive priors with semantic knowledge implicitly captured by an off-the-shelf self-supervised feature extractor. To train such a model, we also contribute a new large-scale dataset of diverse animal species. At inference time, given a single image of any quadruped animal, our model reconstructs an articulated 3D mesh in a feed-forward fashion within seconds. |
|
2024-01-05T00:00:00 | 2401.02117 | Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation | [
"Zipeng Fu",
"Tony Z. Zhao",
"Chelsea Finn"
]
| Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base, and a whole-body teleoperation interface. Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet. Project website: https://mobile-aloha.github.io |
|
2024-01-05T00:00:00 | 2401.02015 | Improving Diffusion-Based Image Synthesis with Context Prediction | [
"Ling Yang",
"Jingwei Liu",
"Shenda Hong",
"Zhilong Zhang",
"Zhilin Huang",
"Zheming Cai",
"Wentao Zhang",
"Bin Cui"
]
| Diffusion models are a new class of generative models, and have dramatically promoted image generation with unprecedented quality and diversity. Existing diffusion models mainly try to reconstruct input image from a corrupted one with a pixel-wise or feature-wise constraint along spatial axes. However, such point-based reconstruction may fail to make each predicted pixel/feature fully preserve its neighborhood context, impairing diffusion-based image synthesis. As a powerful source of automatic supervisory signal, context has been well studied for learning representations. Inspired by this, we for the first time propose ConPreDiff to improve diffusion-based image synthesis with context prediction. We explicitly reinforce each point to predict its neighborhood context (i.e., multi-stride features/tokens/pixels) with a context decoder at the end of diffusion denoising blocks in training stage, and remove the decoder for inference. In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context. This new paradigm of ConPreDiff can generalize to arbitrary discrete and continuous diffusion backbones without introducing extra parameters in sampling procedure. Extensive experiments are conducted on unconditional image generation, text-to-image generation and image inpainting tasks. Our ConPreDiff consistently outperforms previous methods and achieves a new SOTA text-to-image generation results on MS-COCO, with a zero-shot FID score of 6.21. |
|
2024-01-05T00:00:00 | 2401.02330 | LLaVA-φ: Efficient Multi-Modal Assistant with Small Language Model | [
"Yichen Zhu",
"Minjie Zhu",
"Ning Liu",
"Zhicai Ou",
"Xiaofeng Mou",
"Jian Tang"
]
| https://github.com/zhuyiche/llava-phi | In this paper, we introduce LLaVA-phi (LLaVA-Phi), an efficient multi-modal assistant that harnesses the power of the recently advanced small language model, Phi-2, to facilitate multi-modal dialogues. LLaVA-Phi marks a notable advancement in the realm of compact multi-modal models. It demonstrates that even smaller language models, with as few as 2.7B parameters, can effectively engage in intricate dialogues that integrate both textual and visual elements, provided they are trained with high-quality corpora. Our model delivers commendable performance on publicly available benchmarks that encompass visual comprehension, reasoning, and knowledge-based perception. Beyond its remarkable performance in multi-modal dialogue tasks, our model opens new avenues for applications in time-sensitive environments and systems that require real-time interaction, such as embodied agents. It highlights the potential of smaller language models to achieve sophisticated levels of understanding and interaction, while maintaining greater resource efficiency.The project is available at {https://github.com/zhuyiche/llava-phi}. |
2024-01-05T00:00:00 | 2401.01974 | Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers | [
"Aleksandar Stanić",
"Sergi Caelles",
"Michael Tschannen"
]
| Visual reasoning is dominated by end-to-end neural networks scaled to billions of model parameters and training examples. However, even the largest models struggle with compositional reasoning, generalization, fine-grained spatial and temporal reasoning, and counting. Visual reasoning with large language models (LLMs) as controllers can, in principle, address these limitations by decomposing the task and solving subtasks by orchestrating a set of (visual) tools. Recently, these models achieved great performance on tasks such as compositional visual question answering, visual grounding, and video temporal reasoning. Nevertheless, in their current form, these models heavily rely on human engineering of in-context examples in the prompt, which are often dataset- and task-specific and require significant labor by highly skilled programmers. In this work, we present a framework that mitigates these issues by introducing spatially and temporally abstract routines and by leveraging a small number of labeled examples to automatically generate in-context examples, thereby avoiding human-created in-context examples. On a number of visual reasoning tasks, we show that our framework leads to consistent gains in performance, makes LLMs as controllers setup more robust, and removes the need for human engineering of in-context examples. |
|
2024-01-05T00:00:00 | 2401.01970 | FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding | [
"Xingxing Zuo",
"Pouya Samangouei",
"Yunwen Zhou",
"Yan Di",
"Mingyang Li"
]
| Precisely perceiving the geometric and semantic properties of real-world 3D objects is crucial for the continued evolution of augmented reality and robotic applications. To this end, we present (), which incorporates vision-language embeddings of foundation models into 3D Gaussian Splatting (GS). The key contribution of this work is an efficient method to reconstruct and represent 3D vision-language models. This is achieved by distilling feature maps generated from image-based foundation models into those rendered from our 3D model. To ensure high-quality rendering and fast training, we introduce a novel scene representation by integrating strengths from both GS and multi-resolution hash encodings (MHE). Our effective training procedure also introduces a pixel alignment loss that makes the rendered feature distance of same semantic entities close, following the pixel-level semantic boundaries. Our results demonstrate remarkable multi-view semantic consistency, facilitating diverse downstream tasks, beating state-of-the-art methods by 10.2 percent on open-vocabulary language-based object detection, despite that we are 851times faster for inference. This research explores the intersection of vision, language, and 3D scene representation, paving the way for enhanced scene understanding in uncontrolled real-world environments. We plan to release the code upon paper acceptance. |
|
2024-01-08T00:00:00 | 2401.02954 | DeepSeek LLM: Scaling Open-Source Language Models with Longtermism | [
"DeepSeek-AI",
"Xiao Bi",
"Deli Chen",
"Guanting Chen",
"Shanhuang Chen",
"Damai Dai",
"Chengqi Deng",
"Honghui Ding",
"Kai Dong",
"Qiushi Du",
"Zhe Fu",
"Huazuo Gao",
"Kaige Gao",
"Wenjun Gao",
"Ruiqi Ge",
"Kang Guan",
"Daya Guo",
"Jianzhong Guo",
"Guangbo Hao",
"Zhewen Hao",
"Ying He",
"Wenjie Hu",
"Panpan Huang",
"Erhang Li",
"Guowei Li",
"Jiashi Li",
"Yao Li",
"Y. K. Li",
"Wenfeng Liang",
"Fangyun Lin",
"A. X. Liu",
"Bo Liu",
"Wen Liu",
"Xiaodong Liu",
"Xin Liu",
"Yiyuan Liu",
"Haoyu Lu",
"Shanghao Lu",
"Fuli Luo",
"Shirong Ma",
"Xiaotao Nie",
"Tian Pei",
"Yishi Piao",
"Junjie Qiu",
"Hui Qu",
"Tongzheng Ren",
"Zehui Ren",
"Chong Ruan",
"Zhangli Sha",
"Zhihong Shao",
"Junxiao Song",
"Xuecheng Su",
"Jingxiang Sun",
"Yaofeng Sun",
"Minghui Tang",
"Bingxuan Wang",
"Peiyi Wang",
"Shiyu Wang",
"Yaohui Wang",
"Yongji Wang",
"Tong Wu",
"Y. Wu",
"Xin Xie",
"Zhenda Xie",
"Ziwei Xie",
"Yiliang Xiong",
"Hanwei Xu",
"R. X. Xu",
"Yanhong Xu",
"Dejian Yang",
"Yuxiang You",
"Shuiping Yu",
"Xingkai Yu",
"B. Zhang",
"Haowei Zhang",
"Lecong Zhang",
"Liyue Zhang",
"Mingchuan Zhang",
"Minghua Zhang",
"Wentao Zhang",
"Yichao Zhang",
"Chenggang Zhao",
"Yao Zhao",
"Shangyan Zhou",
"Shunfeng Zhou",
"Qihao Zhu",
"Yuheng Zou"
]
| https://github.com/deepseek-ai/DeepSeek-LLM | The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5. |
2024-01-08T00:00:00 | 2401.02677 | Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss | [
"Yatharth Gupta",
"Vishnu V. Jaddipal",
"Harish Prabhala",
"Sayak Paul",
"Patrick Von Platen"
]
| Stable Diffusion XL (SDXL) has become the best open source text-to-image model (T2I) for its versatility and top-notch image quality. Efficiently addressing the computational demands of SDXL models is crucial for wider reach and applicability. In this work, we introduce two scaled-down variants, Segmind Stable Diffusion (SSD-1B) and Segmind-Vega, with 1.3B and 0.74B parameter UNets, respectively, achieved through progressive removal using layer-level losses focusing on reducing the model size while preserving generative quality. We release these models weights at https://hf.co/Segmind. Our methodology involves the elimination of residual networks and transformer blocks from the U-Net structure of SDXL, resulting in significant reductions in parameters, and latency. Our compact models effectively emulate the original SDXL by capitalizing on transferred knowledge, achieving competitive results against larger multi-billion parameter SDXL. Our work underscores the efficacy of knowledge distillation coupled with layer-level losses in reducing model size while preserving the high-quality generative capabilities of SDXL, thus facilitating more accessible deployment in resource-constrained environments. |
|
2024-01-08T00:00:00 | 2401.02839 | Pheme: Efficient and Conversational Speech Generation | [
"Paweł Budzianowski",
"Taras Sereda",
"Tomasz Cichy",
"Ivan Vulić"
]
| In recent years, speech generation has seen remarkable progress, now achieving one-shot generation capability that is often virtually indistinguishable from real human voice. Integrating such advancements in speech generation with large language models might revolutionize a wide range of applications. However, certain applications, such as assistive conversational systems, require natural and conversational speech generation tools that also operate efficiently in real time. Current state-of-the-art models like VALL-E and SoundStorm, powered by hierarchical neural audio codecs, require large neural components and extensive training data to work well. In contrast, MQTTS aims to build more compact conversational TTS models while capitalizing on smaller-scale real-life conversational speech data. However, its autoregressive nature yields high inference latency and thus limits its real-time usage. In order to mitigate the current limitations of the state-of-the-art TTS models while capitalizing on their strengths, in this work we introduce the Pheme model series that 1) offers compact yet high-performing models, 2) allows for parallel speech generation of 3) natural conversational speech, and 4) it can be trained efficiently on smaller-scale conversational data, cutting data demands by more than 10x but still matching the quality of the autoregressive TTS models. We also show that through simple teacher-student distillation we can meet significant improvements in voice quality for single-speaker setups on top of pretrained Pheme checkpoints, relying solely on synthetic speech generated by much larger teacher models. Audio samples and pretrained models are available online. |
|
2024-01-08T00:00:00 | 2401.02669 | Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache | [
"Bin Lin",
"Tao Peng",
"Chen Zhang",
"Minmin Sun",
"Lanbo Li",
"Hanyu Zhao",
"Wencong Xiao",
"Qi Xu",
"Xiafei Qiu",
"Shen Li",
"Zhigang Ji",
"Yong Li",
"Wei Lin"
]
| The rapid proliferation of Large Language Models (LLMs) has been a driving force in the growth of cloud-based LLM services, which are now integral to advancing AI applications. However, the dynamic auto-regressive nature of LLM service, along with the need to support exceptionally long context lengths, demands the flexible allocation and release of substantial resources. This presents considerable challenges in designing cloud-based LLM service systems, where inefficient management can lead to performance degradation or resource wastage. In response to these challenges, this work introduces DistAttention, a novel distributed attention algorithm that segments the KV Cache into smaller, manageable units, enabling distributed processing and storage of the attention module. Based on that, we propose DistKV-LLM, a distributed LLM serving system that dynamically manages KV Cache and effectively orchestrates all accessible GPU and CPU memories spanning across the data center. This ensures a high-performance LLM service on the cloud, adaptable to a broad range of context lengths. Validated in a cloud environment with 32 NVIDIA A100 GPUs in configurations from 2 to 32 instances, our system exhibited 1.03-2.4x end-to-end throughput improvements and supported context lengths 2-19x longer than current state-of-the-art LLM service systems, as evidenced by extensive testing across 18 datasets with context lengths up to 1,900K. |
|
2024-01-08T00:00:00 | 2401.02823 | DocGraphLM: Documental Graph Language Model for Information Extraction | [
"Dongsheng Wang",
"Zhiqiang Ma",
"Armineh Nourbakhsh",
"Kang Gu",
"Sameena Shah"
]
| Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures have emerged -- transformer-based models inspired by LLMs, and Graph Neural Networks. In this paper, we introduce DocGraphLM, a novel framework that combines pre-trained language models with graph semantics. To achieve this, we propose 1) a joint encoder architecture to represent documents, and 2) a novel link prediction approach to reconstruct document graphs. DocGraphLM predicts both directions and distances between nodes using a convergent joint loss function that prioritizes neighborhood restoration and downweighs distant node detection. Our experiments on three SotA datasets show consistent improvement on IE and QA tasks with the adoption of graph features. Moreover, we report that adopting the graph features accelerates convergence in the learning process during training, despite being solely constructed through link prediction. |
|
2024-01-08T00:00:00 | 2401.02957 | Denoising Vision Transformers | [
"Jiawei Yang",
"Katie Z Luo",
"Jiefeng Li",
"Kilian Q Weinberger",
"Yonglong Tian",
"Yue Wang"
]
| We delve into a nuanced but significant challenge inherent to Vision Transformers (ViTs): feature maps of these models exhibit grid-like artifacts, which detrimentally hurt the performance of ViTs in downstream tasks. Our investigations trace this fundamental issue down to the positional embeddings at the input stage. To address this, we propose a novel noise model, which is universally applicable to all ViTs. Specifically, the noise model dissects ViT outputs into three components: a semantics term free from noise artifacts and two artifact-related terms that are conditioned on pixel locations. Such a decomposition is achieved by enforcing cross-view feature consistency with neural fields in a per-image basis. This per-image optimization process extracts artifact-free features from raw ViT outputs, providing clean features for offline applications. Expanding the scope of our solution to support online functionality, we introduce a learnable denoiser to predict artifact-free features directly from unprocessed ViT outputs, which shows remarkable generalization capabilities to novel data without the need for per-image optimization. Our two-stage approach, termed Denoising Vision Transformers (DVT), does not require re-training existing pre-trained ViTs and is immediately applicable to any Transformer-based architecture. We evaluate our method on a variety of representative ViTs (DINO, MAE, DeiT-III, EVA02, CLIP, DINOv2, DINOv2-reg). Extensive evaluations demonstrate that our DVT consistently and significantly improves existing state-of-the-art general-purpose models in semantic and geometric tasks across multiple datasets (e.g., +3.84 mIoU). We hope our study will encourage a re-evaluation of ViT design, especially regarding the naive use of positional embeddings. |
|
2024-01-08T00:00:00 | 2401.02955 | Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively | [
"Haobo Yuan",
"Xiangtai Li",
"Chong Zhou",
"Yining Li",
"Kai Chen",
"Chen Change Loy"
]
| https://github.com/HarborYuan/ovsam | The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. This paper presents an in-depth exploration of integrating these two models into a unified framework. Specifically, we introduce the Open-Vocabulary SAM, a SAM-inspired model designed for simultaneous interactive segmentation and recognition, leveraging two unique knowledge transfer modules: SAM2CLIP and CLIP2SAM. The former adapts SAM's knowledge into the CLIP via distillation and learnable transformer adapters, while the latter transfers CLIP knowledge into SAM, enhancing its recognition capabilities. Extensive experiments on various datasets and detectors show the effectiveness of Open-Vocabulary SAM in both segmentation and recognition tasks, significantly outperforming the naive baselines of simply combining SAM and CLIP. Furthermore, aided with image classification data training, our method can segment and recognize approximately 22,000 classes. |
2024-01-09T00:00:00 | 2401.04088 | Mixtral of Experts | [
"Albert Q. Jiang",
"Alexandre Sablayrolles",
"Antoine Roux",
"Arthur Mensch",
"Blanche Savary",
"Chris Bamford",
"Devendra Singh Chaplot",
"Diego de las Casas",
"Emma Bou Hanna",
"Florian Bressand",
"Gianna Lengyel",
"Guillaume Bour",
"Guillaume Lample",
"Lélio Renard Lavaud",
"Lucile Saulnier",
"Marie-Anne Lachaux",
"Pierre Stock",
"Sandeep Subramanian",
"Sophia Yang",
"Szymon Antoniak",
"Teven Le Scao",
"Théophile Gervet",
"Thibaut Lavril",
"Thomas Wang",
"Timothée Lacroix",
"William El Sayed"
]
| We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two experts, the selected experts can be different at each timestep. As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference. Mixtral was trained with a context size of 32k tokens and it outperforms or matches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks. We also provide a model fine-tuned to follow instructions, Mixtral 8x7B - Instruct, that surpasses GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B - chat model on human benchmarks. Both the base and instruct models are released under the Apache 2.0 license. |
|
2024-01-09T00:00:00 | 2401.04092 | GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation | [
"Tong Wu",
"Guandao Yang",
"Zhibing Li",
"Kai Zhang",
"Ziwei Liu",
"Leonidas Guibas",
"Dahua Lin",
"Gordon Wetzstein"
]
| Despite recent advances in text-to-3D generative methods, there is a notable absence of reliable evaluation metrics. Existing metrics usually focus on a single criterion each, such as how well the asset aligned with the input text. These metrics lack the flexibility to generalize to different evaluation criteria and might not align well with human preferences. Conducting user preference studies is an alternative that offers both adaptability and human-aligned results. User studies, however, can be very expensive to scale. This paper presents an automatic, versatile, and human-aligned evaluation metric for text-to-3D generative models. To this end, we first develop a prompt generator using GPT-4V to generate evaluating prompts, which serve as input to compare text-to-3D models. We further design a method instructing GPT-4V to compare two 3D assets according to user-defined criteria. Finally, we use these pairwise comparison results to assign these models Elo ratings. Experimental results suggest our metric strongly align with human preference across different evaluation criteria. |
|
2024-01-09T00:00:00 | 2401.03065 | CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution | [
"Alex Gu",
"Baptiste Rozière",
"Hugh Leather",
"Armando Solar-Lezama",
"Gabriel Synnaeve",
"Sida I. Wang"
]
| We present CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation), a benchmark consisting of 800 Python functions (3-13 lines). Each function comes with an input-output pair, leading to two natural tasks: input prediction and output prediction. First, we propose a generic recipe for generating our execution benchmark which can be used to create future variation of the benchmark. Second, we evaluate twenty code models on our benchmark and discover that many recent high-scoring models on HumanEval do not show the same improvements on our benchmark. Third, we show that simple CoT and fine-tuning schemes can improve performance on our benchmark but remain far from solving it. The best setup, GPT-4 with chain of thought (CoT), achieves a pass@1 of 75% and 81% on input and output prediction, respectively. In contrast, Code Llama 34B achieves a pass@1 of 50% and 46% on input and output prediction, highlighting the gap between open and closed source models. As no model is close to acing CRUXEval, we provide examples of consistent GPT-4 failures on simple programs as a lens into its code reasoning capabilities and areas for improvement. |
|
2024-01-09T00:00:00 | 2401.03462 | Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon | [
"Peitian Zhang",
"Zheng Liu",
"Shitao Xiao",
"Ninglu Shao",
"Qiwei Ye",
"Zhicheng Dou"
]
| The utilization of long contexts poses a big challenge for large language models due to their limited context window length. Although the context window can be extended through fine-tuning, it will result in a considerable cost at both training and inference time, and exert an unfavorable impact to the LLM's original capabilities. In this work, we propose Activation Beacon, which condenses LLM's raw activations into more compact forms such that it can perceive a much longer context with a limited context window. Activation Beacon is introduced as a plug-and-play module for the LLM. It fully preserves the LLM's original capability on short contexts while extending the new capability on processing longer contexts. Besides, it works with short sliding windows to process the long context, which achieves a competitive memory and time efficiency in both training and inference. Activation Beacon is learned by the auto-regression task conditioned on a mixture of beacons with diversified condensing ratios. Thanks to such a treatment, it can be efficiently trained purely with short-sequence data in just 10K steps, which consumes less than 9 hours on a single 8xA800 GPU machine. The experimental studies show that Activation Beacon is able to extend Llama-2-7B's context length by times100 times (from 4K to 400K), meanwhile achieving a superior result on both long-context generation and understanding tasks. Our model and code will be available at the BGE repository. |
|
2024-01-09T00:00:00 | 2401.03003 | AST-T5: Structure-Aware Pretraining for Code Generation and Understanding | [
"Linyuan Gong",
"Mostafa Elhoushi",
"Alvin Cheung"
]
| https://github.com/gonglinyuan/ast_t5 | Large language models (LLMs) have made significant advancements in code-related tasks, yet many LLMs treat code as simple sequences, neglecting its structured nature. We introduce AST-T5, a novel pretraining paradigm that leverages the Abstract Syntax Tree (AST) for enhanced code generation, transpilation, and understanding. Using dynamic programming, our AST-Aware Segmentation retains code structure, while our AST-Aware Span Corruption objective equips the model to reconstruct various code structures. Unlike other models, AST-T5 avoids intricate program analyses or architectural changes, so it integrates seamlessly with any encoder-decoder Transformer. Evaluations show that AST-T5 consistently outperforms similar-sized LMs across various code-related tasks. Structure-awareness makes AST-T5 particularly powerful in code-to-code tasks, surpassing CodeT5 by 2 points in exact match score for the Bugs2Fix task and by 3 points in exact match score for Java-C# Transpilation in CodeXGLUE. Our code and model are publicly available at https://github.com/gonglinyuan/ast_t5. |
2024-01-09T00:00:00 | 2401.02987 | Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach | [
"Prince Aboagye",
"Yan Zheng",
"Junpeng Wang",
"Uday Singh Saini",
"Xin Dai",
"Michael Yeh",
"Yujie Fan",
"Zhongfang Zhuang",
"Shubham Jain",
"Liang Wang",
"Wei Zhang"
]
| The emergence of pretrained models has significantly impacted from Natural Language Processing (NLP) and Computer Vision to relational datasets. Traditionally, these models are assessed through fine-tuned downstream tasks. However, this raises the question of how to evaluate these models more efficiently and more effectively. In this study, we explore a novel approach where we leverage the meta features associated with each entity as a source of worldly knowledge and employ entity representations from the models. We propose using the consistency between these representations and the meta features as a metric for evaluating pretrained models. Our method's effectiveness is demonstrated across various domains, including models with relational datasets, large language models and images models. |
|
2024-01-09T00:00:00 | 2401.02994 | Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM | [
"Xiaoding Lu",
"Adian Liusie",
"Vyas Raina",
"Yuwen Zhang",
"William Beauchamp"
]
| In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand significant computational resources and memory. This study explores a pertinent question: Can a combination of smaller models collaboratively achieve comparable or enhanced performance relative to a singular large model? We introduce an approach termed "blending", a straightforward yet effective method of integrating multiple chat AIs. Our empirical evidence suggests that when specific smaller models are synergistically blended, they can potentially outperform or match the capabilities of much larger counterparts. For instance, integrating just three models of moderate size (6B/13B paramaeters) can rival or even surpass the performance metrics of a substantially larger model like ChatGPT (175B+ paramaters). This hypothesis is rigorously tested using A/B testing methodologies with a large user base on the Chai research platform over a span of thirty days. The findings underscore the potential of the "blending" strategy as a viable approach for enhancing chat AI efficacy without a corresponding surge in computational demands. |
|
2024-01-09T00:00:00 | 2401.04081 | MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts | [
"Maciej Pióro",
"Kamil Ciebiera",
"Krystian Król",
"Jan Ludziejewski",
"Sebastian Jaszczur"
]
| State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers. At the same time, Mixture of Experts (MoE) has significantly improved Transformer-based LLMs, including recent state-of-the-art open-source models. We propose that to unlock the potential of SSMs for scaling, they should be combined with MoE. We showcase this on Mamba, a recent SSM-based model that achieves remarkable, Transformer-like performance. Our model, MoE-Mamba, outperforms both Mamba and Transformer-MoE. In particular, MoE-Mamba reaches the same performance as Mamba in 2.2x less training steps while preserving the inference performance gains of Mamba against the Transformer. |
|
2024-01-09T00:00:00 | 2401.03804 | TeleChat Technical Report | [
"Zihan Wang",
"Xinzhang Liu",
"Shixuan Liu",
"Yitong Yao",
"Yuyao Huang",
"Zhongjiang He",
"Xuelong Li",
"Yongxiang Li",
"Zhonghao Che",
"Zhaoxi Zhang",
"Yan Wang",
"Xin Wang",
"Luwen Pu",
"Huihan Xu",
"Ruiyu Fang",
"Yu Zhao",
"Jie Zhang",
"Xiaomeng Huang",
"Zhilong Lu",
"Jiaxin Peng",
"Wenjun Zheng",
"Shiquan Wang",
"Bingkai Yang",
"Xuewei he",
"Zhuoru Jiang",
"Qiyi Xie",
"Yanhan Zhang",
"Zhongqiu Li",
"Lingling Shi",
"Weiwei Fu",
"Yin Zhang",
"Zilu Huang",
"Sishi Xiong",
"Yuxiang Zhang",
"Chao Wang",
"Shuangyong Song"
]
| In this technical report, we present TeleChat, a collection of large language models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It includes pretrained language models as well as fine-tuned chat models that is aligned with human preferences. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, including trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves comparable performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat's 7B and 12B variant, along with code and a portion of our pretraining data, to the public community. |
|
2024-01-09T00:00:00 | 2401.04099 | AGG: Amortized Generative 3D Gaussians for Single Image to 3D | [
"Dejia Xu",
"Ye Yuan",
"Morteza Mardani",
"Sifei Liu",
"Jiaming Song",
"Zhangyang Wang",
"Arash Vahdat"
]
| Given the growing need for automatic 3D content creation pipelines, various 3D representations have been studied to generate 3D objects from a single image. Due to its superior rendering efficiency, 3D Gaussian splatting-based models have recently excelled in both 3D reconstruction and generation. 3D Gaussian splatting approaches for image to 3D generation are often optimization-based, requiring many computationally expensive score-distillation steps. To overcome these challenges, we introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image, eliminating the need for per-instance optimization. Utilizing an intermediate hybrid representation, AGG decomposes the generation of 3D Gaussian locations and other appearance attributes for joint optimization. Moreover, we propose a cascaded pipeline that first generates a coarse representation of the 3D data and later upsamples it with a 3D Gaussian super-resolution module. Our method is evaluated against existing optimization-based 3D Gaussian frameworks and sampling-based pipelines utilizing other 3D representations, where AGG showcases competitive generation abilities both qualitatively and quantitatively while being several orders of magnitude faster. Project page: https://ir1d.github.io/AGG/ |
|
2024-01-09T00:00:00 | 2401.03506 | DiarizationLM: Speaker Diarization Post-Processing with Large Language Models | [
"Quan Wang",
"Yiling Huang",
"Guanlong Zhao",
"Evan Clark",
"Wei Xia",
"Hank Liao"
]
| In this paper, we introduce DiarizationLM, a framework to leverage large language models (LLM) to post-process the outputs from a speaker diarization system. Various goals can be achieved with the proposed framework, such as improving the readability of the diarized transcript, or reducing the word diarization error rate (WDER). In this framework, the outputs of the automatic speech recognition (ASR) and speaker diarization systems are represented as a compact textual format, which is included in the prompt to an optionally finetuned LLM. The outputs of the LLM can be used as the refined diarization results with the desired enhancement. As a post-processing step, this framework can be easily applied to any off-the-shelf ASR and speaker diarization systems without retraining existing components. Our experiments show that a finetuned PaLM 2-S model can reduce the WDER by rel. 25.9% on the Fisher telephone conversation dataset, and rel. 31% on the Callhome English dataset. |
|
2024-01-10T00:00:00 | 2401.04468 | MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation | [
"Weimin Wang",
"Jiawei Liu",
"Zhijie Lin",
"Jiangqiao Yan",
"Shuo Chen",
"Chetwin Low",
"Tuyen Hoang",
"Jie Wu",
"Jun Hao Liew",
"Hanshu Yan",
"Daquan Zhou",
"Jiashi Feng"
]
| The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field. In this work, we introduce MagicVideo-V2 that integrates the text-to-image model, video motion generator, reference image embedding module and frame interpolation module into an end-to-end video generation pipeline. Benefiting from these architecture designs, MagicVideo-V2 can generate an aesthetically pleasing, high-resolution video with remarkable fidelity and smoothness. It demonstrates superior performance over leading Text-to-Video systems such as Runway, Pika 1.0, Morph, Moon Valley and Stable Video Diffusion model via user evaluation at large scale. |
|
2024-01-10T00:00:00 | 2401.04575 | Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding | [
"Yatong Bai",
"Utsav Garg",
"Apaar Shanker",
"Haoming Zhang",
"Samyak Parajuli",
"Erhan Bas",
"Isidora Filipovic",
"Amelia N. Chu",
"Eugenia D Fomitcheva",
"Elliot Branson",
"Aerin Kim",
"Somayeh Sojoudi",
"Kyunghyun Cho"
]
| Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes. This time-consuming endeavor hinders the emergence of large-scale datasets, limiting researchers and practitioners to a small number of choices. Therefore, we seek more efficient ways to collect and annotate images. Previous initiatives have gathered captions from HTML alt-texts and crawled social media postings, but these data sources suffer from noise, sparsity, or subjectivity. For this reason, we turn to commercial shopping websites whose data meet three criteria: cleanliness, informativeness, and fluency. We introduce the Let's Go Shopping (LGS) dataset, a large-scale public dataset with 15 million image-caption pairs from publicly available e-commerce websites. When compared with existing general-domain datasets, the LGS images focus on the foreground object and have less complex backgrounds. Our experiments on LGS show that the classifiers trained on existing benchmark datasets do not readily generalize to e-commerce data, while specific self-supervised visual feature extractors can better generalize. Furthermore, LGS's high-quality e-commerce-focused images and bimodal nature make it advantageous for vision-language bi-modal tasks: LGS enables image-captioning models to generate richer captions and helps text-to-image generation models achieve e-commerce style transfer. |
|
2024-01-10T00:00:00 | 2401.04577 | Masked Audio Generation using a Single Non-Autoregressive Transformer | [
"Alon Ziv",
"Itai Gat",
"Gael Le Lan",
"Tal Remez",
"Felix Kreuk",
"Alexandre Défossez",
"Jade Copet",
"Gabriel Synnaeve",
"Yossi Adi"
]
| We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of audio tokens. Unlike prior work, MAGNeT is comprised of a single-stage, non-autoregressive transformer. During training, we predict spans of masked tokens obtained from a masking scheduler, while during inference we gradually construct the output sequence using several decoding steps. To further enhance the quality of the generated audio, we introduce a novel rescoring method in which, we leverage an external pre-trained model to rescore and rank predictions from MAGNeT, which will be then used for later decoding steps. Lastly, we explore a hybrid version of MAGNeT, in which we fuse between autoregressive and non-autoregressive models to generate the first few seconds in an autoregressive manner while the rest of the sequence is being decoded in parallel. We demonstrate the efficiency of MAGNeT for the task of text-to-music and text-to-audio generation and conduct an extensive empirical evaluation, considering both objective metrics and human studies. The proposed approach is comparable to the evaluated baselines, while being significantly faster (x7 faster than the autoregressive baseline). Through ablation studies and analysis, we shed light on the importance of each of the components comprising MAGNeT, together with pointing to the trade-offs between autoregressive and non-autoregressive modeling, considering latency, throughput, and generation quality. Samples are available on our demo page https://pages.cs.huji.ac.il/adiyoss-lab/MAGNeT. |
|
2024-01-10T00:00:00 | 2401.04695 | Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers | [
"Gal Yona",
"Roee Aharoni",
"Mor Geva"
]
| Factual questions typically can be answered correctly at different levels of granularity. For example, both ``August 4, 1961'' and ``1961'' are correct answers to the question ``When was Barack Obama born?''. Standard question answering (QA) evaluation protocols, however, do not explicitly take this into account and compare a predicted answer against answers of a single granularity level. In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers. We present a simple methodology for enriching existing datasets with multi-granularity answers, and create GRANOLA-EQ, a multi-granularity version of the EntityQuestions dataset. We evaluate a range of decoding methods on GRANOLA-EQ, including a new algorithm, called Decoding with Response Aggregation (DRAG), that is geared towards aligning the response granularity with the model's uncertainty. Our experiments show that large language models with standard decoding tend to generate specific answers, which are often incorrect. In contrast, when evaluated on multi-granularity answers, DRAG yields a nearly 20 point increase in accuracy on average, which further increases for rare entities. Overall, this reveals that standard evaluation and decoding schemes may significantly underestimate the knowledge encapsulated in LMs. |
|
2024-01-10T00:00:00 | 2401.04658 | Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models | [
"Zhen Qin",
"Weigao Sun",
"Dong Li",
"Xuyang Shen",
"Weixuan Sun",
"Yiran Zhong"
]
| https://github.com/OpenNLPLab/lightning-attention | Linear attention is an efficient attention mechanism that has recently emerged as a promising alternative to conventional softmax attention. With its ability to process tokens in linear computational complexities, linear attention, in theory, can handle sequences of unlimited length without sacrificing speed, i.e., maintaining a constant training speed for various sequence lengths with a fixed memory consumption. However, due to the issue with cumulative summation (cumsum), current linear attention algorithms cannot demonstrate their theoretical advantage in a causal setting. In this paper, we present Lightning Attention-2, the first linear attention implementation that enables linear attention to realize its theoretical computational benefits. To achieve this, we leverage the thought of tiling, separately handling the intra-block and inter-block components in linear attention calculation. Specifically, we utilize the conventional attention computation mechanism for the intra-blocks and apply linear attention kernel tricks for the inter-blocks. A tiling technique is adopted through both forward and backward procedures to take full advantage of the GPU hardware. We implement our algorithm in Triton to make it IO-aware and hardware-friendly. Various experiments are conducted on different model sizes and sequence lengths. Lightning Attention-2 retains consistent training and inference speed regardless of input sequence length and is significantly faster than other attention mechanisms. The source code is available at https://github.com/OpenNLPLab/lightning-attention. |
2024-01-10T00:00:00 | 2401.04398 | Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding | [
"Zilong Wang",
"Hao Zhang",
"Chun-Liang Li",
"Julian Martin Eisenschlos",
"Vincent Perot",
"Zifeng Wang",
"Lesly Miculicich",
"Yasuhisa Fujii",
"Jingbo Shang",
"Chen-Yu Lee",
"Tomas Pfister"
]
| Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices. |
|
2024-01-10T00:00:00 | 2401.04718 | Jump Cut Smoothing for Talking Heads | [
"Xiaojuan Wang",
"Taesung Park",
"Yang Zhou",
"Eli Shechtman",
"Richard Zhang"
]
| A jump cut offers an abrupt, sometimes unwanted change in the viewing experience. We present a novel framework for smoothing these jump cuts, in the context of talking head videos. We leverage the appearance of the subject from the other source frames in the video, fusing it with a mid-level representation driven by DensePose keypoints and face landmarks. To achieve motion, we interpolate the keypoints and landmarks between the end frames around the cut. We then use an image translation network from the keypoints and source frames, to synthesize pixels. Because keypoints can contain errors, we propose a cross-modal attention scheme to select and pick the most appropriate source amongst multiple options for each key point. By leveraging this mid-level representation, our method can achieve stronger results than a strong video interpolation baseline. We demonstrate our method on various jump cuts in the talking head videos, such as cutting filler words, pauses, and even random cuts. Our experiments show that we can achieve seamless transitions, even in the challenging cases where the talking head rotates or moves drastically in the jump cut. |
|
2024-01-10T00:00:00 | 2401.04283 | FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation | [
"Yang Liu",
"Li Wan",
"Yun Li",
"Yiteng Huang",
"Ming Sun",
"James Luan",
"Yangyang Shi",
"Xin Lei"
]
| Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted. In this paper, we propose DI-AEC, pioneering a diffusion-based stochastic regeneration approach dedicated to AEC. Further, we propose FADI-AEC, fast score-based diffusion AEC framework to save computational demands, making it favorable for edge devices. It stands out by running the score model once per frame, achieving a significant surge in processing efficiency. Apart from that, we introduce a novel noise generation technique where far-end signals are utilized, incorporating both far-end and near-end signals to refine the score model's accuracy. We test our proposed method on the ICASSP2023 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the end-to-end methods and other diffusion based echo cancellation methods. |
|
2024-01-11T00:00:00 | 2401.05252 | PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models | [
"Junsong Chen",
"Yue Wu",
"Simian Luo",
"Enze Xie",
"Sayak Paul",
"Ping Luo",
"Hang Zhao",
"Zhenguo Li"
]
| This technical report introduces PIXART-{\delta}, a text-to-image synthesis framework that integrates the Latent Consistency Model (LCM) and ControlNet into the advanced PIXART-{\alpha} model. PIXART-{\alpha} is recognized for its ability to generate high-quality images of 1024px resolution through a remarkably efficient training process. The integration of LCM in PIXART-{\delta} significantly accelerates the inference speed, enabling the production of high-quality images in just 2-4 steps. Notably, PIXART-{\delta} achieves a breakthrough 0.5 seconds for generating 1024x1024 pixel images, marking a 7x improvement over the PIXART-{\alpha}. Additionally, PIXART-{\delta} is designed to be efficiently trainable on 32GB V100 GPUs within a single day. With its 8-bit inference capability (von Platen et al., 2023), PIXART-{\delta} can synthesize 1024px images within 8GB GPU memory constraints, greatly enhancing its usability and accessibility. Furthermore, incorporating a ControlNet-like module enables fine-grained control over text-to-image diffusion models. We introduce a novel ControlNet-Transformer architecture, specifically tailored for Transformers, achieving explicit controllability alongside high-quality image generation. As a state-of-the-art, open-source image generation model, PIXART-{\delta} offers a promising alternative to the Stable Diffusion family of models, contributing significantly to text-to-image synthesis. |
|
2024-01-11T00:00:00 | 2401.05334 | URHand: Universal Relightable Hands | [
"Zhaoxi Chen",
"Gyeongsik Moon",
"Kaiwen Guo",
"Chen Cao",
"Stanislav Pidhorskyi",
"Tomas Simon",
"Rohan Joshi",
"Yuan Dong",
"Yichen Xu",
"Bernardo Pires",
"He Wen",
"Lucas Evans",
"Bo Peng",
"Julia Buffalini",
"Autumn Trimble",
"Kevyn McPhail",
"Melissa Schoeller",
"Shoou-I Yu",
"Javier Romero",
"Michael Zollhöfer",
"Yaser Sheikh",
"Ziwei Liu",
"Shunsuke Saito"
]
| Existing photorealistic relightable hand models require extensive identity-specific observations in different views, poses, and illuminations, and face challenges in generalizing to natural illuminations and novel identities. To bridge this gap, we present URHand, the first universal relightable hand model that generalizes across viewpoints, poses, illuminations, and identities. Our model allows few-shot personalization using images captured with a mobile phone, and is ready to be photorealistically rendered under novel illuminations. To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities. The key challenge is scaling the cross-identity training while maintaining personalized fidelity and sharp details without compromising generalization under natural illuminations. To this end, we propose a spatially varying linear lighting model as the neural renderer that takes physics-inspired shading as input feature. By removing non-linear activations and bias, our specifically designed lighting model explicitly keeps the linearity of light transport. This enables single-stage training from light-stage data while generalizing to real-time rendering under arbitrary continuous illuminations across diverse identities. In addition, we introduce the joint learning of a physically based model and our neural relighting model, which further improves fidelity and generalization. Extensive experiments show that our approach achieves superior performance over existing methods in terms of both quality and generalizability. We also demonstrate quick personalization of URHand from a short phone scan of an unseen identity. |
|
2024-01-11T00:00:00 | 2401.05335 | InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes | [
"Mohamad Shahbazi",
"Liesbeth Claessens",
"Michael Niemeyer",
"Edo Collins",
"Alessio Tonioni",
"Luc Van Gool",
"Federico Tombari"
]
| We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes. Based on a user-provided textual description and a 2D bounding box in a reference viewpoint, InseRF generates new objects in 3D scenes. Recently, methods for 3D scene editing have been profoundly transformed, owing to the use of strong priors of text-to-image diffusion models in 3D generative modeling. Existing methods are mostly effective in editing 3D scenes via style and appearance changes or removing existing objects. Generating new objects, however, remains a challenge for such methods, which we address in this study. Specifically, we propose grounding the 3D object insertion to a 2D object insertion in a reference view of the scene. The 2D edit is then lifted to 3D using a single-view object reconstruction method. The reconstructed object is then inserted into the scene, guided by the priors of monocular depth estimation methods. We evaluate our method on various 3D scenes and provide an in-depth analysis of the proposed components. Our experiments with generative insertion of objects in several 3D scenes indicate the effectiveness of our method compared to the existing methods. InseRF is capable of controllable and 3D-consistent object insertion without requiring explicit 3D information as input. Please visit our project page at https://mohamad-shahbazi.github.io/inserf. |
|
2024-01-11T00:00:00 | 2401.04925 | The Impact of Reasoning Step Length on Large Language Models | [
"Mingyu Jin",
"Qinkai Yu",
"Dong shu",
"Haiyan Zhao",
"Wenyue Hua",
"Yanda Meng",
"Yongfeng Zhang",
"Mengnan Du"
]
| Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical experiments to explore the relations. Specifically, we design experiments that expand and compress the rationale reasoning steps within CoT demonstrations, while keeping all other factors constant. We have the following key findings. First, the results indicate that lengthening the reasoning steps in prompts, even without adding new information into the prompt, considerably enhances LLMs' reasoning abilities across multiple datasets. Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models. This finding highlights the importance of the number of steps in CoT prompts and provides practical guidance to make better use of LLMs' potential in complex problem-solving scenarios. Second, we also investigated the relationship between the performance of CoT and the rationales used in demonstrations. Surprisingly, the result shows that even incorrect rationales can yield favorable outcomes if they maintain the requisite length of inference. Third, we observed that the advantages of increasing reasoning steps are task-dependent: simpler tasks require fewer steps, whereas complex tasks gain significantly from longer inference sequences. |
|
2024-01-11T00:00:00 | 2401.05033 | Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk | [
"Dennis Ulmer",
"Elman Mansimov",
"Kaixiang Lin",
"Justin Sun",
"Xibin Gao",
"Yi Zhang"
]
| Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be challenging. Instructing tuning, i.e. tuning models on instruction and sample responses generated by humans (Ouyang et al., 2022), has proven as an effective method to do so, yet requires a number of data samples that a) might not be available or b) costly to generate. Furthermore, this cost increases when the goal is to make the LLM follow a specific workflow within a dialogue instead of single instructions. Inspired by the self-play technique in reinforcement learning and the use of LLMs to simulate human agents, we propose a more effective method for data collection through LLMs engaging in a conversation in various roles. This approach generates a training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. We introduce an automated way to measure the (partial) success of a dialogue. This metric is used to filter the generated conversational data that is fed back in LLM for training. Based on our automated and human evaluations of conversation quality, we demonstrate that such self-talk data improves results. In addition, we examine the various characteristics that showcase the quality of generated dialogues and how they can be connected to their potential utility as training data. |
|
2024-01-11T00:00:00 | 2401.05314 | ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video | [
"Kevin Cai",
"Chonghua Liu",
"David M. Chan"
]
| https://github.com/davidmchan/Anim400K | The Internet's wealth of content, with up to 60% published in English, starkly contrasts the global population, where only 18.8% are English speakers, and just 5.1% consider it their native language, leading to disparities in online information access. Unfortunately, automated processes for dubbing of video - replacing the audio track of a video with a translated alternative - remains a complex and challenging task due to pipelines, necessitating precise timing, facial movement synchronization, and prosody matching. While end-to-end dubbing offers a solution, data scarcity continues to impede the progress of both end-to-end and pipeline-based methods. In this work, we introduce Anim-400K, a comprehensive dataset of over 425K aligned animated video segments in Japanese and English supporting various video-related tasks, including automated dubbing, simultaneous translation, guided video summarization, and genre/theme/style classification. Our dataset is made publicly available for research purposes at https://github.com/davidmchan/Anim400K. |
2024-01-11T00:00:00 | 2401.05293 | Score Distillation Sampling with Learned Manifold Corrective | [
"Thiemo Alldieck",
"Nikos Kolotouros",
"Cristian Sminchisescu"
]
| Score Distillation Sampling (SDS) is a recent but already widely popular method that relies on an image diffusion model to control optimization problems using text prompts. In this paper, we conduct an in-depth analysis of the SDS loss function, identify an inherent problem with its formulation, and propose a surprisingly easy but effective fix. Specifically, we decompose the loss into different factors and isolate the component responsible for noisy gradients. In the original formulation, high text guidance is used to account for the noise, leading to unwanted side effects. Instead, we train a shallow network mimicking the timestep-dependent denoising deficiency of the image diffusion model in order to effectively factor it out. We demonstrate the versatility and the effectiveness of our novel loss formulation through several qualitative and quantitative experiments, including optimization-based image synthesis and editing, zero-shot image translation network training, and text-to-3D synthesis. |
|
2024-01-12T00:00:00 | 2401.06104 | Transformers are Multi-State RNNs | [
"Matanel Oren",
"Michael Hassid",
"Yossi Adi",
"Roy Schwartz"
]
| https://github.com/schwartz-lab-NLP/TOVA | Transformers are considered conceptually different compared to the previous generation of state-of-the-art NLP models - recurrent neural networks (RNNs). In this work, we demonstrate that decoder-only transformers can in fact be conceptualized as infinite multi-state RNNs - an RNN variant with unlimited hidden state size. We further show that pretrained transformers can be converted into finite multi-state RNNs by fixing the size of their hidden state. We observe that several existing transformers cache compression techniques can be framed as such conversion policies, and introduce a novel policy, TOVA, which is simpler compared to these policies. Our experiments with several long range tasks indicate that TOVA outperforms all other baseline policies, while being nearly on par with the full (infinite) model, and using in some cases only 1{8} of the original cache size. Our results indicate that transformer decoder LLMs often behave in practice as RNNs. They also lay out the option of mitigating one of their most painful computational bottlenecks - the size of their cache memory. We publicly release our code at https://github.com/schwartz-lab-NLP/TOVA. |
2024-01-12T00:00:00 | 2401.05675 | Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation | [
"Seung Hyun Lee",
"Yinxiao Li",
"Junjie Ke",
"Innfarn Yoo",
"Han Zhang",
"Jiahui Yu",
"Qifei Wang",
"Fei Deng",
"Glenn Entis",
"Junfeng He",
"Gang Li",
"Sangpil Kim",
"Irfan Essa",
"Feng Yang"
]
| Recent works demonstrate that using reinforcement learning (RL) with quality rewards can enhance the quality of generated images in text-to-image (T2I) generation. However, a simple aggregation of multiple rewards may cause over-optimization in certain metrics and degradation in others, and it is challenging to manually find the optimal weights. An effective strategy to jointly optimize multiple rewards in RL for T2I generation is highly desirable. This paper introduces Parrot, a novel multi-reward RL framework for T2I generation. Through the use of the batch-wise Pareto optimal selection, Parrot automatically identifies the optimal trade-off among different rewards during the RL optimization of the T2I generation. Additionally, Parrot employs a joint optimization approach for the T2I model and the prompt expansion network, facilitating the generation of quality-aware text prompts, thus further enhancing the final image quality. To counteract the potential catastrophic forgetting of the original user prompt due to prompt expansion, we introduce original prompt centered guidance at inference time, ensuring that the generated image remains faithful to the user input. Extensive experiments and a user study demonstrate that Parrot outperforms several baseline methods across various quality criteria, including aesthetics, human preference, image sentiment, and text-image alignment. |
|
2024-01-12T00:00:00 | 2401.06102 | Patchscope: A Unifying Framework for Inspecting Hidden Representations of Language Models | [
"Asma Ghandeharioun",
"Avi Caciularu",
"Adam Pearce",
"Lucas Dixon",
"Mor Geva"
]
| Inspecting the information encoded in hidden representations of large language models (LLMs) can explain models' behavior and verify their alignment with human values. Given the capabilities of LLMs in generating human-understandable text, we propose leveraging the model itself to explain its internal representations in natural language. We introduce a framework called Patchscopes and show how it can be used to answer a wide range of research questions about an LLM's computation. We show that prior interpretability methods based on projecting representations into the vocabulary space and intervening on the LLM computation, can be viewed as special instances of this framework. Moreover, several of their shortcomings such as failure in inspecting early layers or lack of expressivity can be mitigated by a Patchscope. Beyond unifying prior inspection techniques, Patchscopes also opens up new possibilities such as using a more capable model to explain the representations of a smaller model, and unlocks new applications such as self-correction in multi-hop reasoning. |
|
2024-01-12T00:00:00 | 2401.05654 | Towards Conversational Diagnostic AI | [
"Tao Tu",
"Anil Palepu",
"Mike Schaekermann",
"Khaled Saab",
"Jan Freyberg",
"Ryutaro Tanno",
"Amy Wang",
"Brenna Li",
"Mohamed Amin",
"Nenad Tomasev",
"Shekoofeh Azizi",
"Karan Singhal",
"Yong Cheng",
"Le Hou",
"Albert Webson",
"Kavita Kulkarni",
"S Sara Mahdavi",
"Christopher Semturs",
"Juraj Gottweis",
"Joelle Barral",
"Katherine Chou",
"Greg S Corrado",
"Yossi Matias",
"Alan Karthikesalingam",
"Vivek Natarajan"
]
| At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI. |
|
2024-01-12T00:00:00 | 2401.06066 | DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models | [
"Damai Dai",
"Chengqi Deng",
"Chenggang Zhao",
"R. X. Xu",
"Huazuo Gao",
"Deli Chen",
"Jiashi Li",
"Wangding Zeng",
"Xingkai Yu",
"Y. Wu",
"Zhenda Xie",
"Y. K. Li",
"Panpan Huang",
"Fuli Luo",
"Chong Ruan",
"Zhifang Sui",
"Wenfeng Liang"
]
| https://github.com/deepseek-ai/DeepSeek-MoE | In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-K out of N experts, face challenges in ensuring expert specialization, i.e. each expert acquires non-overlapping and focused knowledge. In response, we propose the DeepSeekMoE architecture towards ultimate expert specialization. It involves two principal strategies: (1) finely segmenting the experts into mN ones and activating mK from them, allowing for a more flexible combination of activated experts; (2) isolating K_s experts as shared ones, aiming at capturing common knowledge and mitigating redundancy in routed experts. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 times the expert parameters and computation. In addition, DeepSeekMoE 2B nearly approaches the performance of its dense counterpart with the same number of total parameters, which set the upper bound of MoE models. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations. Further, our preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently validate its substantial advantages over the GShard architecture, and show its performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations. |
2024-01-12T00:00:00 | 2401.05561 | TrustLLM: Trustworthiness in Large Language Models | [
"Lichao Sun",
"Yue Huang",
"Haoran Wang",
"Siyuan Wu",
"Qihui Zhang",
"Chujie Gao",
"Yixin Huang",
"Wenhan Lyu",
"Yixuan Zhang",
"Xiner Li",
"Zhengliang Liu",
"Yixin Liu",
"Yijue Wang",
"Zhikun Zhang",
"Bhavya Kailkhura",
"Caiming Xiong",
"Chao Zhang",
"Chaowei Xiao",
"Chunyuan Li",
"Eric Xing",
"Furong Huang",
"Hao Liu",
"Heng Ji",
"Hongyi Wang",
"Huan Zhang",
"Huaxiu Yao",
"Manolis Kellis",
"Marinka Zitnik",
"Meng Jiang",
"Mohit Bansal",
"James Zou",
"Jian Pei",
"Jian Liu",
"Jianfeng Gao",
"Jiawei Han",
"Jieyu Zhao",
"Jiliang Tang",
"Jindong Wang",
"John Mitchell",
"Kai Shu",
"Kaidi Xu",
"Kai-Wei Chang",
"Lifang He",
"Lifu Huang",
"Michael Backes",
"Neil Zhenqiang Gong",
"Philip S. Yu",
"Pin-Yu Chen",
"Quanquan Gu",
"Ran Xu",
"Rex Ying",
"Shuiwang Ji",
"Suman Jana",
"Tianlong Chen",
"Tianming Liu",
"Tianyi Zhou",
"Willian Wang",
"Xiang Li",
"Xiangliang Zhang",
"Xiao Wang",
"Xing Xie",
"Xun Chen",
"Xuyu Wang",
"Yan Liu",
"Yanfang Ye",
"Yinzhi Cao",
"Yue Zhao"
]
| Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and utility (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Finally, we emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. Knowing the specific trustworthy technologies that have been employed is crucial for analyzing their effectiveness. |
|
2024-01-12T00:00:00 | 2401.05583 | Diffusion Priors for Dynamic View Synthesis from Monocular Videos | [
"Chaoyang Wang",
"Peiye Zhuang",
"Aliaksandr Siarohin",
"Junli Cao",
"Guocheng Qian",
"Hsin-Ying Lee",
"Sergey Tulyakov"
]
| Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. Existing methods struggle to distinguishing between motion and structure, particularly in scenarios where camera poses are either unknown or constrained compared to object motion. Furthermore, with information solely from reference images, it is extremely challenging to hallucinate unseen regions that are occluded or partially observed in the given videos. To address these issues, we first finetune a pretrained RGB-D diffusion model on the video frames using a customization technique. Subsequently, we distill the knowledge from the finetuned model to a 4D representations encompassing both dynamic and static Neural Radiance Fields (NeRF) components. The proposed pipeline achieves geometric consistency while preserving the scene identity. We perform thorough experiments to evaluate the efficacy of the proposed method qualitatively and quantitatively. Our results demonstrate the robustness and utility of our approach in challenging cases, further advancing dynamic novel view synthesis. |
|
2024-01-12T00:00:00 | 2401.06121 | TOFU: A Task of Fictitious Unlearning for LLMs | [
"Pratyush Maini",
"Zhili Feng",
"Avi Schwarzschild",
"Zachary C. Lipton",
"J. Zico Kolter"
]
| Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns. Unlearning, or tuning models to forget information present in their training data, provides us with a way to protect private data after training. Although several methods exist for such unlearning, it is unclear to what extent they result in models equivalent to those where the data to be forgotten was never learned in the first place. To address this challenge, we present TOFU, a Task of Fictitious Unlearning, as a benchmark aimed at helping deepen our understanding of unlearning. We offer a dataset of 200 diverse synthetic author profiles, each consisting of 20 question-answer pairs, and a subset of these profiles called the forget set that serves as the target for unlearning. We compile a suite of metrics that work together to provide a holistic picture of unlearning efficacy. Finally, we provide a set of baseline results from existing unlearning algorithms. Importantly, none of the baselines we consider show effective unlearning motivating continued efforts to develop approaches for unlearning that effectively tune models so that they truly behave as if they were never trained on the forget data at all. |
|
2024-01-12T00:00:00 | 2401.06071 | LEGO:Language Enhanced Multi-modal Grounding Model | [
"Zhaowei Li",
"Qi Xu",
"Dong Zhang",
"Hang Song",
"Yiqing Cai",
"Qi Qi",
"Ran Zhou",
"Junting Pan",
"Zefeng Li",
"Van Tu Vu",
"Zhida Huang",
"Tao Wang"
]
| Multi-modal large language models have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize capturing global information within each modality while neglecting the importance of perceiving local information across modalities. Consequently, these models lack the ability to effectively understand the fine-grained details of input data, limiting their performance in tasks that require a more nuanced understanding. To address this limitation, there is a compelling need to develop models that enable fine-grained understanding across multiple modalities, thereby enhancing their applicability to a wide range of tasks. In this paper, we propose LEGO, a language enhanced multi-modal grounding model. Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input. It demonstrates precise identification and localization of specific regions in images or moments in videos. To achieve this objective, we design a diversified dataset construction pipeline, resulting in a multi-modal, multi-granularity dataset for model training. The code, dataset, and demo of our model can be found at https: //github.com/lzw-lzw/LEGO. |
|
2024-01-12T00:00:00 | 2401.05566 | Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training | [
"Evan Hubinger",
"Carson Denison",
"Jesse Mu",
"Mike Lambert",
"Meg Tong",
"Monte MacDiarmid",
"Tamera Lanham",
"Daniel M. Ziegler",
"Tim Maxwell",
"Newton Cheng",
"Adam Jermyn",
"Amanda Askell",
"Ansh Radhakrishnan",
"Cem Anil",
"David Duvenaud",
"Deep Ganguli",
"Fazl Barez",
"Jack Clark",
"Kamal Ndousse",
"Kshitij Sachan",
"Michael Sellitto",
"Mrinank Sharma",
"Nova DasSarma",
"Roger Grosse",
"Shauna Kravec",
"Yuntao Bai",
"Zachary Witten",
"Marina Favaro",
"Jan Brauner",
"Holden Karnofsky",
"Paul Christiano",
"Samuel R. Bowman",
"Logan Graham",
"Jared Kaplan",
"Sören Mindermann",
"Ryan Greenblatt",
"Buck Shlegeris",
"Nicholas Schiefer",
"Ethan Perez"
]
| Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoored behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoored behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety. |
|
2024-01-12T00:00:00 | 2401.05391 | Efficient LLM inference solution on Intel GPU | [
"Hui Wu",
"Yi Gan",
"Feng Yuan",
"Jing Ma",
"Wei Zhu",
"Yutao Xu",
"Hong Zhu",
"Yuhua Zhu",
"Xiaoli Liu",
"Jinghui Gu"
]
| Transformer based Large Language Models (LLMs) have been widely used in many fields, and the efficiency of LLM inference becomes hot topic in real applications. However, LLMs are usually complicatedly designed in model structure with massive operations and perform inference in the auto-regressive mode, making it a challenging task to design a system with high efficiency. In this paper, we propose an efficient LLM inference solution with low latency and high throughput. Firstly, we simplify the LLM decoder layer by fusing data movement and element-wise operations to reduce the memory access frequency and lower system latency. We also propose a segment KV cache policy to keep key/value of the request and response tokens in separate physical memory for effective device memory management, helping enlarge the runtime batch size and improve system throughput. A customized Scaled-Dot-Product-Attention kernel is designed to match our fusion policy based on the segment KV cache solution. We implement our LLM inference solution on Intel GPU and publish it publicly. Compared with the standard HuggingFace implementation, the proposed solution achieves up to 7x lower token latency and 27x higher throughput for some popular LLMs on Intel GPU. |
|
2024-01-12T00:00:00 | 2401.06003 | TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Rendering | [
"Linus Franke",
"Darius Rückert",
"Laura Fink",
"Marc Stamminger"
]
| Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage. |
|
2024-01-12T00:00:00 | 2401.06080 | Secrets of RLHF in Large Language Models Part II: Reward Modeling | [
"Binghai Wang",
"Rui Zheng",
"Lu Chen",
"Yan Liu",
"Shihan Dou",
"Caishuang Huang",
"Wei Shen",
"Senjie Jin",
"Enyu Zhou",
"Chenyu Shi",
"Songyang Gao",
"Nuo Xu",
"Yuhao Zhou",
"Xiaoran Fan",
"Zhiheng Xi",
"Jun Zhao",
"Xiao Wang",
"Tao Ji",
"Hang Yan",
"Lixing Shen",
"Zhan Chen",
"Tao Gui",
"Qi Zhang",
"Xipeng Qiu",
"Xuanjing Huang",
"Zuxuan Wu",
"Yu-Gang Jiang"
]
| Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization. |
|
2024-01-12T00:00:00 | 2401.05811 | Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages | [
"Zhuoyuan Mao",
"Yen Yu"
]
| This article introduces contrastive alignment instructions (AlignInstruct) to address two challenges in machine translation (MT) on large language models (LLMs). One is the expansion of supported languages to previously unseen ones. The second relates to the lack of data in low-resource languages. Model fine-tuning through MT instructions (MTInstruct) is a straightforward approach to the first challenge. However, MTInstruct is limited by weak cross-lingual signals inherent in the second challenge. AlignInstruct emphasizes cross-lingual supervision via a cross-lingual discriminator built using statistical word alignments. Our results based on fine-tuning the BLOOMZ models (1b1, 3b, and 7b1) in up to 24 unseen languages showed that: (1) LLMs can effectively translate unseen languages using MTInstruct; (2) AlignInstruct led to consistent improvements in translation quality across 48 translation directions involving English; (3) Discriminator-based instructions outperformed their generative counterparts as cross-lingual instructions; (4) AlignInstruct improved performance in 30 zero-shot directions. |
|
2024-01-12T00:00:00 | 2401.05749 | A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism | [
"Brian Thompson",
"Mehak Preet Dhaliwal",
"Peter Frisch",
"Tobias Domhan",
"Marcello Federico"
]
| We show that content on the web is often translated into many languages, and the low quality of these multi-way translations indicates they were likely created using Machine Translation (MT). Multi-way parallel, machine generated content not only dominates the translations in lower resource languages; it also constitutes a large fraction of the total web content in those languages. We also find evidence of a selection bias in the type of content which is translated into many languages, consistent with low quality English content being translated en masse into many lower resource languages, via MT. Our work raises serious concerns about training models such as multilingual large language models on both monolingual and bilingual data scraped from the web. |
|
2024-01-12T00:00:00 | 2401.06129 | Distilling Vision-Language Models on Millions of Videos | [
"Yue Zhao",
"Long Zhao",
"Xingyi Zhou",
"Jialin Wu",
"Chun-Te Chu",
"Hui Miao",
"Florian Schroff",
"Hartwig Adam",
"Ting Liu",
"Boqing Gong",
"Philipp Krähenbühl",
"Liangzhe Yuan"
]
| The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human-curated video-text data available. We thus resort to fine-tuning a video-language model from a strong image-language baseline with synthesized instructional data. The resulting video-language model is then used to auto-label millions of videos to generate high-quality captions. We show the adapted video-language model performs well on a wide range of video-language benchmarks. For instance, it surpasses the best prior result on open-ended NExT-QA by 2.8%. Besides, our model generates detailed descriptions for previously unseen videos, which provide better textual supervision than existing methods. Experiments show that a video-language dual-encoder model contrastively trained on these auto-generated captions is 3.8% better than the strongest baseline that also leverages vision-language models. Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%. |
|
2024-01-12T00:00:00 | 2401.06105 | PALP: Prompt Aligned Personalization of Text-to-Image Models | [
"Moab Arar",
"Andrey Voynov",
"Amir Hertz",
"Omri Avrahami",
"Shlomi Fruchter",
"Yael Pritch",
"Daniel Cohen-Or",
"Ariel Shamir"
]
| Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques. |
|
2024-01-12T00:00:00 | 2401.05735 | Object-Centric Diffusion for Efficient Video Editing | [
"Kumara Kahatapitiya",
"Adil Karjauv",
"Davide Abati",
"Fatih Porikli",
"Yuki M. Asano",
"Amirhossein Habibian"
]
| Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and suggest simple yet effective modifications that allow significant speed-ups whilst maintaining quality. Moreover, we introduce Object-Centric Diffusion, coined as OCD, to further reduce latency by allocating computations more towards foreground edited regions that are arguably more important for perceptual quality. We achieve this by two novel proposals: i) Object-Centric Sampling, decoupling the diffusion steps spent on salient regions or background, allocating most of the model capacity to the former, and ii) Object-Centric 3D Token Merging, which reduces cost of cross-frame attention by fusing redundant tokens in unimportant background regions. Both techniques are readily applicable to a given video editing model without retraining, and can drastically reduce its memory and computational cost. We evaluate our proposals on inversion-based and control-signal-based editing pipelines, and show a latency reduction up to 10x for a comparable synthesis quality. |
|
2024-01-17T00:00:00 | 2401.08541 | Scalable Pre-training of Large Autoregressive Image Models | [
"Alaaeldin El-Nouby",
"Michal Klein",
"Shuangfei Zhai",
"Miguel Angel Bautista",
"Alexander Toshev",
"Vaishaal Shankar",
"Joshua M Susskind",
"Armand Joulin"
]
| This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks. We illustrate the practical implication of these findings by pre-training a 7 billion parameter AIM on 2 billion images, that achieves 84.0% on ImageNet-1k with a frozen trunk. Interestingly, even at this scale, we observe no sign of saturation in performance, suggesting that AIM potentially represents a new frontier for training large-scale vision models. The pre-training of AIM is similar to the pre-training of LLMs, and does not require any image-specific strategy to stabilize the training at scale. |
|
2024-01-17T00:00:00 | 2401.07519 | InstantID: Zero-shot Identity-Preserving Generation in Seconds | [
"Qixun Wang",
"Xu Bai",
"Haofan Wang",
"Zekui Qin",
"Anthony Chen"
]
| https://github.com/InstantID/InstantID | There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by high storage demands, lengthy fine-tuning processes, and the need for multiple reference images. Conversely, existing ID embedding-based methods, while requiring only a single forward inference, face challenges: they either necessitate extensive fine-tuning across numerous model parameters, lack compatibility with community pre-trained models, or fail to maintain high face fidelity. Addressing these limitations, we introduce InstantID, a powerful diffusion model-based solution. Our plug-and-play module adeptly handles image personalization in various styles using just a single facial image, while ensuring high fidelity. To achieve this, we design a novel IdentityNet by imposing strong semantic and weak spatial conditions, integrating facial and landmark images with textual prompts to steer the image generation. InstantID demonstrates exceptional performance and efficiency, proving highly beneficial in real-world applications where identity preservation is paramount. Moreover, our work seamlessly integrates with popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving as an adaptable plugin. Our codes and pre-trained checkpoints will be available at https://github.com/InstantID/InstantID. |
2024-01-17T00:00:00 | 2401.08417 | Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation | [
"Haoran Xu",
"Amr Sharaf",
"Yunmo Chen",
"Weiting Tan",
"Lingfeng Shen",
"Benjamin Van Durme",
"Kenton Murray",
"Young Jin Kim"
]
| Moderate-sized large language models (LLMs) -- those with 7B or 13B parameters -- exhibit promising machine translation (MT) performance. However, even the top-performing 13B LLM-based translation models, like ALMA, does not match the performance of state-of-the-art conventional encoder-decoder translation models or larger-scale LLMs such as GPT-4. In this study, we bridge this performance gap. We first assess the shortcomings of supervised fine-tuning for LLMs in the MT task, emphasizing the quality issues present in the reference data, despite being human-generated. Then, in contrast to SFT which mimics reference translations, we introduce Contrastive Preference Optimization (CPO), a novel approach that trains models to avoid generating adequate but not perfect translations. Applying CPO to ALMA models with only 22K parallel sentences and 12M parameters yields significant improvements. The resulting model, called ALMA-R, can match or exceed the performance of the WMT competition winners and GPT-4 on WMT'21, WMT'22 and WMT'23 test datasets. |
|
2024-01-17T00:00:00 | 2401.07781 | Towards A Better Metric for Text-to-Video Generation | [
"Jay Zhangjie Wu",
"Guian Fang",
"Haoning Wu",
"Xintao Wang",
"Yixiao Ge",
"Xiaodong Cun",
"David Junhao Zhang",
"Jia-Wei Liu",
"Yuchao Gu",
"Rui Zhao",
"Weisi Lin",
"Wynne Hsu",
"Ying Shan",
"Mike Zheng Shou"
]
| Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos. For video generation, contemporary text-to-video models exhibit impressive capabilities, crafting visually stunning videos. Nonetheless, evaluating such videos poses significant challenges. Current research predominantly employs automated metrics such as FVD, IS, and CLIP Score. However, these metrics provide an incomplete analysis, particularly in the temporal assessment of video content, thus rendering them unreliable indicators of true video quality. Furthermore, while user studies have the potential to reflect human perception accurately, they are hampered by their time-intensive and laborious nature, with outcomes that are often tainted by subjective bias. In this paper, we investigate the limitations inherent in existing metrics and introduce a novel evaluation pipeline, the Text-to-Video Score (T2VScore). This metric integrates two pivotal criteria: (1) Text-Video Alignment, which scrutinizes the fidelity of the video in representing the given text description, and (2) Video Quality, which evaluates the video's overall production caliber with a mixture of experts. Moreover, to evaluate the proposed metrics and facilitate future improvements on them, we present the TVGE dataset, collecting human judgements of 2,543 text-to-video generated videos on the two criteria. Experiments on the TVGE dataset demonstrate the superiority of the proposed T2VScore on offering a better metric for text-to-video generation. |
|
2024-01-17T00:00:00 | 2401.08565 | Tuning Language Models by Proxy | [
"Alisa Liu",
"Xiaochuang Han",
"Yizhong Wang",
"Yulia Tsvetkov",
"Yejin Choi",
"Noah A. Smith"
]
| Despite the general capabilities of large pretrained language models, they consistently benefit from further adaptation to better achieve desired behaviors. However, tuning these models has become increasingly resource-intensive, or impossible when model weights are private. We introduce proxy-tuning, a lightweight decoding-time algorithm that operates on top of black-box LMs to achieve the result of directly tuning the model, but by accessing only its prediction over the output vocabulary. Our method instead tunes a smaller LM, then applies the difference between the predictions of the small tuned and untuned LMs to shift the original predictions of the base model in the direction of tuning, while retaining the benefits of larger scale pretraining. In experiments, when we apply proxy-tuning to Llama2-70B using proxies of only 7B size, we can close 88% of the gap between Llama2-70B and its truly-tuned chat version, when evaluated across knowledge, reasoning, and safety benchmarks. Interestingly, when tested on TruthfulQA, proxy-tuned models are actually more truthful than directly tuned models, possibly because decoding-time guidance better retains the model's factual knowledge. We then demonstrate the generality of proxy-tuning by applying it for domain adaptation on code, and task-specific finetuning on question-answering and math problems. Our work demonstrates the promise of using small tuned LMs to efficiently customize large, potentially proprietary LMs through decoding-time guidance. |
|
2024-01-17T00:00:00 | 2401.07049 | Quantum Denoising Diffusion Models | [
"Michael Kölle",
"Gerhard Stenzel",
"Jonas Stein",
"Sebastian Zielinski",
"Björn Ommer",
"Claudia Linnhoff-Popien"
]
| In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions. Concurrently, quantum computing is showing promising advances, especially with quantum machine learning which capitalizes on quantum mechanics to meet the increasing computational requirements of traditional machine learning algorithms. This paper explores the integration of quantum machine learning and variational quantum circuits to augment the efficacy of diffusion-based image generation models. Specifically, we address two challenges of classical diffusion models: their low sampling speed and the extensive parameter requirements. We introduce two quantum diffusion models and benchmark their capabilities against their classical counterparts using MNIST digits, Fashion MNIST, and CIFAR-10. Our models surpass the classical models with similar parameter counts in terms of performance metrics FID, SSIM, and PSNR. Moreover, we introduce a consistency model unitary single sampling architecture that combines the diffusion procedure into a single step, enabling a fast one-step image generation. |
|
2024-01-17T00:00:00 | 2401.07004 | Extending LLMs' Context Window with 100 Samples | [
"Yikai Zhang",
"Junlong Li",
"Pengfei Liu"
]
| https://github.com/GAIR-NLP/Entropy-ABF | Large Language Models (LLMs) are known to have limited extrapolation ability beyond their pre-trained context window, constraining their application in downstream tasks with lengthy inputs. Recent studies have sought to extend LLMs' context window by modifying rotary position embedding (RoPE), a popular position encoding method adopted by well-known LLMs such as LLaMA, PaLM, and GPT-NeoX. However, prior works like Position Interpolation (PI) and YaRN are resource-intensive and lack comparative experiments to assess their applicability. In this work, we identify the inherent need for LLMs' attention entropy (i.e. the information entropy of attention scores) to maintain stability and introduce a novel extension to RoPE which combines adjusting RoPE's base frequency and scaling the attention logits to help LLMs efficiently adapt to a larger context window. We validate the superiority of our method in both fine-tuning performance and robustness across different context window sizes on various context-demanding tasks. Notably, our method extends the context window of LLaMA-2-7B-Chat to 16,384 with only 100 samples and 6 training steps, showcasing extraordinary efficiency. Finally, we also explore how data compositions and training curricula affect context window extension for specific downstream tasks, suggesting fine-tuning LLMs with lengthy conversations as a good starting point. We release our code and SFT data at https://github.com/GAIR-NLP/Entropy-ABF. |
2024-01-17T00:00:00 | 2401.06951 | E^2-LLM: Efficient and Extreme Length Extension of Large Language Models | [
"Jiaheng Liu",
"Zhiqi Bai",
"Yuanxing Zhang",
"Chenchen Zhang",
"Yu Zhang",
"Ge Zhang",
"Jiakai Wang",
"Haoran Que",
"Yukang Chen",
"Wenbo Su",
"Tiezheng Ge",
"Jie Fu",
"Wenhu Chen",
"Bo Zheng"
]
| Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. Existing long-context extension methods usually need additional training procedures to support corresponding long-context windows, where the long-context training data (e.g., 32k) is needed, and high GPU training costs are assumed. To address the aforementioned issues, we propose an Efficient and Extreme length extension method for Large Language Models, called E 2 -LLM, with only one training procedure and dramatically reduced computation cost, which also removes the need to collect long-context data. Concretely, first, the training data of our E 2 -LLM only requires a short length (e.g., 4k), which reduces the tuning cost greatly. Second, the training procedure on the short training context window is performed only once time, and we can support different evaluation context windows at inference. Third, in E 2 - LLM, based on RoPE position embeddings, we introduce two different augmentation methods on the scale and position index parameters for different samples in training. It aims to make the model more robust to the different relative differences when directly interpolating the arbitrary context length at inference. Comprehensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our E 2 -LLM on challenging long-context tasks. |
|
2024-01-17T00:00:00 | 2401.07727 | HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation | [
"Antoine Mercier",
"Ramin Nakhli",
"Mahesh Reddy",
"Rajeev Yasarla",
"Hong Cai",
"Fatih Porikli",
"Guillaume Berger"
]
| Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task. A key challenge lies in data scarcity: the most extensive 3D datasets encompass merely millions of assets, while their 2D counterparts contain billions of text-image pairs. To address this, we propose a novel approach which harnesses the power of large, pretrained 2D diffusion models. More specifically, our approach, HexaGen3D, fine-tunes a pretrained text-to-image model to jointly predict 6 orthographic projections and the corresponding latent triplane. We then decode these latents to generate a textured mesh. HexaGen3D does not require per-sample optimization, and can infer high-quality and diverse objects from textual prompts in 7 seconds, offering significantly better quality-to-latency trade-offs when comparing to existing approaches. Furthermore, HexaGen3D demonstrates strong generalization to new objects or compositions. |
|
2024-01-18T00:00:00 | 2401.08967 | ReFT: Reasoning with Reinforced Fine-Tuning | [
"Trung Quoc Luong",
"Xinbo Zhang",
"Zhanming Jie",
"Peng Sun",
"Xiaoran Jin",
"Hang Li"
]
| One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does not show sufficiently strong generalization ability, however, because the training only relies on the given CoT data. In math problem-solving, for example, there is usually only one annotated reasoning path for each question in the training data. Intuitively, it would be better for the algorithm to learn from multiple annotated reasoning paths given a question. To address this issue, we propose a simple yet effective approach called Reinforced Fine-Tuning (ReFT) to enhance the generalizability of learning LLMs for reasoning, with math problem-solving as an example. ReFT first warmups the model with SFT, and then employs on-line reinforcement learning, specifically the PPO algorithm in this paper, to further fine-tune the model, where an abundance of reasoning paths are automatically sampled given the question and the rewards are naturally derived from the ground-truth answers. Extensive experiments on GSM8K, MathQA, and SVAMP datasets show that ReFT significantly outperforms SFT, and the performance can be potentially further boosted by combining inference-time strategies such as majority voting and re-ranking. Note that ReFT obtains the improvement by learning from the same training questions as SFT, without relying on extra or augmented training questions. This indicates a superior generalization ability for ReFT. |
|
2024-01-18T00:00:00 | 2401.08671 | DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference | [
"Connor Holmes",
"Masahiro Tanaka",
"Michael Wyatt",
"Ammar Ahmad Awan",
"Jeff Rasley",
"Samyam Rajbhandari",
"Reza Yazdani Aminabadi",
"Heyang Qin",
"Arash Bakhtiari",
"Lev Kurilenko",
"Yuxiong He"
]
| The deployment and scaling of large language models (LLMs) have become critical as they permeate various applications, demanding high-throughput and low-latency serving systems. Existing frameworks struggle to balance these requirements, especially for workloads with long prompts. This paper introduces DeepSpeed-FastGen, a system that employs Dynamic SplitFuse, a novel prompt and generation composition strategy, to deliver up to 2.3x higher effective throughput, 2x lower latency on average, and up to 3.7x lower (token-level) tail latency, compared to state-of-the-art systems like vLLM. We leverage a synergistic combination of DeepSpeed-MII and DeepSpeed-Inference to provide an efficient and easy-to-use serving system for LLMs. DeepSpeed-FastGen's advanced implementation supports a range of models and offers both non-persistent and persistent deployment options, catering to diverse user scenarios from interactive sessions to long-running applications. We present a detailed benchmarking methodology, analyze the performance through latency-throughput curves, and investigate scalability via load balancing. Our evaluations demonstrate substantial improvements in throughput and latency across various models and hardware configurations. We discuss our roadmap for future enhancements, including broader model support and new hardware backends. The DeepSpeed-FastGen code is readily available for community engagement and contribution. |
|
2024-01-18T00:00:00 | 2401.09084 | UniVG: Towards UNIfied-modal Video Generation | [
"Ludan Ruan",
"Lei Tian",
"Chuanwei Huang",
"Xu Zhang",
"Xinyan Xiao"
]
| Diffusion based video generation has received extensive attention and achieved considerable success within both the academic and industrial communities. However, current efforts are mainly concentrated on single-objective or single-task video generation, such as generation driven by text, by image, or by a combination of text and image. This cannot fully meet the needs of real-world application scenarios, as users are likely to input images and text conditions in a flexible manner, either individually or in combination. To address this, we propose a Unified-modal Video Genearation system that is capable of handling multiple video generation tasks across text and image modalities. To this end, we revisit the various video generation tasks within our system from the perspective of generative freedom, and classify them into high-freedom and low-freedom video generation categories. For high-freedom video generation, we employ Multi-condition Cross Attention to generate videos that align with the semantics of the input images or text. For low-freedom video generation, we introduce Biased Gaussian Noise to replace the pure random Gaussian Noise, which helps to better preserve the content of the input conditions. Our method achieves the lowest Fr\'echet Video Distance (FVD) on the public academic benchmark MSR-VTT, surpasses the current open-source methods in human evaluations, and is on par with the current close-source method Gen2. For more samples, visit https://univg-baidu.github.io. |
|
2024-01-18T00:00:00 | 2401.09047 | VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models | [
"Haoxin Chen",
"Yong Zhang",
"Xiaodong Cun",
"Menghan Xia",
"Xintao Wang",
"Chao Weng",
"Ying Shan"
]
| https://github.com/AILab-CVC/VideoCrafter | Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition. |
2024-01-18T00:00:00 | 2401.09340 | SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding | [
"Baoxiong Jia",
"Yixin Chen",
"Huangyue Yu",
"Yan Wang",
"Xuesong Niu",
"Tengyu Liu",
"Qing Li",
"Siyuan Huang"
]
| 3D vision-language grounding, which focuses on aligning language with the 3D physical environment, stands as a cornerstone in the development of embodied agents. In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces several significant challenges: (i) the inherent complexity of 3D scenes due to the diverse object configurations, their rich attributes, and intricate relationships; (ii) the scarcity of paired 3D vision-language data to support grounded learning; and (iii) the absence of a unified learning framework to distill knowledge from grounded 3D data. In this work, we aim to address these three major challenges in 3D vision-language by examining the potential of systematically upscaling 3D vision-language learning in indoor environments. We introduce the first million-scale 3D vision-language dataset, SceneVerse, encompassing about 68K 3D indoor scenes and comprising 2.5M vision-language pairs derived from both human annotations and our scalable scene-graph-based generation approach. We demonstrate that this scaling allows for a unified pre-training framework, Grounded Pre-training for Scenes (GPS), for 3D vision-language learning. Through extensive experiments, we showcase the effectiveness of GPS by achieving state-of-the-art performance on all existing 3D visual grounding benchmarks. The vast potential of SceneVerse and GPS is unveiled through zero-shot transfer experiments in the challenging 3D vision-language tasks. Project website: https://scene-verse.github.io . |
|
2024-01-18T00:00:00 | 2401.09417 | Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model | [
"Lianghui Zhu",
"Bencheng Liao",
"Qian Zhang",
"Xinlong Wang",
"Wenyu Liu",
"Xinggang Wang"
]
| https://github.com/hustvl/Vim | Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., Mamba, have shown great potential for long sequence modeling. Building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement of global context for visual understanding. In this paper, we show that the reliance of visual representation learning on self-attention is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. On ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks, Vim achieves higher performance compared to well-established vision transformers like DeiT, while also demonstrating significantly improved computation & memory efficiency. For example, Vim is 2.8times faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on images with a resolution of 1248times1248. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to become the next-generation backbone for vision foundation models. Code is available at https://github.com/hustvl/Vim. |
2024-01-18T00:00:00 | 2401.08740 | SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers | [
"Nanye Ma",
"Mark Goldstein",
"Michael S. Albergo",
"Nicholas M. Boffi",
"Eric Vanden-Eijnden",
"Saining Xie"
]
| We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which allows for connecting two distributions in a more flexible way than standard diffusion models, makes possible a modular study of various design choices impacting generative models built on dynamical transport: using discrete vs. continuous time learning, deciding the objective for the model to learn, choosing the interpolant connecting the distributions, and deploying a deterministic or stochastic sampler. By carefully introducing the above ingredients, SiT surpasses DiT uniformly across model sizes on the conditional ImageNet 256x256 benchmark using the exact same backbone, number of parameters, and GFLOPs. By exploring various diffusion coefficients, which can be tuned separately from learning, SiT achieves an FID-50K score of 2.06. |
|
2024-01-18T00:00:00 | 2401.09135 | Asynchronous Local-SGD Training for Language Modeling | [
"Bo Liu",
"Rachita Chhaparia",
"Arthur Douillard",
"Satyen Kale",
"Andrei A. Rusu",
"Jiajun Shen",
"Arthur Szlam",
"Marc'Aurelio Ranzato"
]
| Local stochastic gradient descent (Local-SGD), also referred to as federated averaging, is an approach to distributed optimization where each device performs more than one SGD update per communication. This work presents an empirical study of {\it asynchronous} Local-SGD for training language models; that is, each worker updates the global parameters as soon as it has finished its SGD steps. We conduct a comprehensive investigation by examining how worker hardware heterogeneity, model size, number of workers, and optimizer could impact the learning performance. We find that with naive implementations, asynchronous Local-SGD takes more iterations to converge than its synchronous counterpart despite updating the (global) model parameters more frequently. We identify momentum acceleration on the global parameters when worker gradients are stale as a key challenge. We propose a novel method that utilizes a delayed Nesterov momentum update and adjusts the workers' local training steps based on their computation speed. This approach, evaluated with models up to 150M parameters on the C4 dataset, matches the performance of synchronous Local-SGD in terms of perplexity per update step, and significantly surpasses it in terms of wall clock time. |
|
2024-01-18T00:00:00 | 2401.09419 | GARField: Group Anything with Radiance Fields | [
"Chung Min Kim",
"Mingxuan Wu",
"Justin Kerr",
"Ken Goldberg",
"Matthew Tancik",
"Angjoo Kanazawa"
]
| Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene -- should the wheels of an excavator be considered separate or part of the whole? We present Group Anything with Radiance Fields (GARField), an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field, a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy, using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects, objects, and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding. See the project website at https://www.garfield.studio/ |
|
2024-01-18T00:00:00 | 2401.09048 | Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis | [
"Jonghyun Lee",
"Hansam Cho",
"Youngjoon Yoo",
"Seoung Bum Kim",
"Yonghyun Jeong"
]
| https://github.com/tomtom1103/compose-and-conquer | Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certain attributes within a generated image. Although successful, previous works do not account for the specific localization of said attributes extended into the three dimensional plane. In this context, we present a conditional diffusion model that integrates control over three-dimensional object placement with disentangled representations of global stylistic semantics from multiple exemplar images. Specifically, we first introduce depth disentanglement training to leverage the relative depth of objects as an estimator, allowing the model to identify the absolute positions of unseen objects through the use of synthetic image triplets. We also introduce soft guidance, a method for imposing global semantics onto targeted regions without the use of any additional localization cues. Our integrated framework, Compose and Conquer (CnC), unifies these techniques to localize multiple conditions in a disentangled manner. We demonstrate that our approach allows perception of objects at varying depths while offering a versatile framework for composing localized objects with different global semantics. Code: https://github.com/tomtom1103/compose-and-conquer/ |
2024-01-18T00:00:00 | 2401.08937 | ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization | [
"Weiyao Wang",
"Pierre Gleize",
"Hao Tang",
"Xingyu Chen",
"Kevin J Liang",
"Matt Feiszli"
]
| Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However, NeRF training requires accurate camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint, but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON), an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further, ICON introduces ``confidence": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF, and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON, without prior pose initialization, achieves superior performance in both CO3D and HO3D versus methods which use SfM pose. |
|
2024-01-18T00:00:00 | 2401.09416 | TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion | [
"Yu-Ying Yeh",
"Jia-Bin Huang",
"Changil Kim",
"Lei Xiao",
"Thu Nguyen-Phuoc",
"Numair Khan",
"Cheng Zhang",
"Manmohan Chandraker",
"Carl S Marshall",
"Zhao Dong",
"Zhengqin Li"
]
| We present TextureDreamer, a novel image-guided texture synthesis method to transfer relightable textures from a small number of input images (3 to 5) to target 3D shapes across arbitrary categories. Texture creation is a pivotal challenge in vision and graphics. Industrial companies hire experienced artists to manually craft textures for 3D assets. Classical methods require densely sampled views and accurately aligned geometry, while learning-based methods are confined to category-specific shapes within the dataset. In contrast, TextureDreamer can transfer highly detailed, intricate textures from real-world environments to arbitrary objects with only a few casually captured images, potentially significantly democratizing texture creation. Our core idea, personalized geometry-aware score distillation (PGSD), draws inspiration from recent advancements in diffuse models, including personalized modeling for texture information extraction, variational score distillation for detailed appearance synthesis, and explicit geometry guidance with ControlNet. Our integration and several essential modifications substantially improve the texture quality. Experiments on real images spanning different categories show that TextureDreamer can successfully transfer highly realistic, semantic meaningful texture to arbitrary objects, surpassing the visual quality of previous state-of-the-art. |
|
2024-01-19T00:00:00 | 2401.10020 | Self-Rewarding Language Models | [
"Weizhe Yuan",
"Richard Yuanzhe Pang",
"Kyunghyun Cho",
"Sainbayar Sukhbaatar",
"Jing Xu",
"Jason Weston"
]
| We posit that to achieve superhuman agents, future models require superhuman feedback in order to provide an adequate training signal. Current approaches commonly train reward models from human preferences, which may then be bottlenecked by human performance level, and secondly these separate frozen reward models cannot then learn to improve during LLM training. In this work, we study Self-Rewarding Language Models, where the language model itself is used via LLM-as-a-Judge prompting to provide its own rewards during training. We show that during Iterative DPO training that not only does instruction following ability improve, but also the ability to provide high-quality rewards to itself. Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613. While only a preliminary study, this work opens the door to the possibility of models that can continually improve in both axes. |
|
2024-01-19T00:00:00 | 2401.09985 | WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens | [
"Xiaofeng Wang",
"Zheng Zhu",
"Guan Huang",
"Boyuan Wang",
"Xinze Chen",
"Jiwen Lu"
]
| World models play a crucial role in understanding and predicting the dynamics of the world, which is essential for video generation. However, existing world models are confined to specific scenarios such as gaming or driving, limiting their ability to capture the complexity of general world dynamic environments. Therefore, we introduce WorldDreamer, a pioneering world model to foster a comprehensive comprehension of general world physics and motions, which significantly enhances the capabilities of video generation. Drawing inspiration from the success of large language models, WorldDreamer frames world modeling as an unsupervised visual sequence modeling challenge. This is achieved by mapping visual inputs to discrete tokens and predicting the masked ones. During this process, we incorporate multi-modal prompts to facilitate interaction within the world model. Our experiments show that WorldDreamer excels in generating videos across different scenarios, including natural scenes and driving environments. WorldDreamer showcases versatility in executing tasks such as text-to-video conversion, image-tovideo synthesis, and video editing. These results underscore WorldDreamer's effectiveness in capturing dynamic elements within diverse general world environments. |
|
2024-01-19T00:00:00 | 2401.09962 | CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects | [
"Zhao Wang",
"Aoxue Li",
"Enze Xie",
"Lingting Zhu",
"Yong Guo",
"Qi Dou",
"Zhenguo Li"
]
| Customized text-to-video generation aims to generate high-quality videos guided by text prompts and subject references. Current approaches designed for single subjects suffer from tackling multiple subjects, which is a more challenging and practical scenario. In this work, we aim to promote multi-subject guided text-to-video customization. We propose CustomVideo, a novel framework that can generate identity-preserving videos with the guidance of multiple subjects. To be specific, firstly, we encourage the co-occurrence of multiple subjects via composing them in a single image. Further, upon a basic text-to-video diffusion model, we design a simple yet effective attention control strategy to disentangle different subjects in the latent space of diffusion model. Moreover, to help the model focus on the specific object area, we segment the object from given reference images and provide a corresponding object mask for attention learning. Also, we collect a multi-subject text-to-video generation dataset as a comprehensive benchmark, with 69 individual subjects and 57 meaningful pairs. Extensive qualitative, quantitative, and user study results demonstrate the superiority of our method, compared with the previous state-of-the-art approaches. |
|
2024-01-19T00:00:00 | 2401.10061 | DiffusionGPT: LLM-Driven Text-to-Image Generation System | [
"Jie Qin",
"Jie Wu",
"Weifeng Chen",
"Yuxi Ren",
"Huixia Li",
"Hefeng Wu",
"Xuefeng Xiao",
"Rui Wang",
"Shilei Wen"
]
| Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms. However, a major challenge persists in current text-to-image systems are often unable to handle diverse inputs, or are limited to single model results. Current unified attempts often fall into two orthogonal aspects: i) parse Diverse Prompts in input stage; ii) activate expert model to output. To combine the best of both worlds, we propose DiffusionGPT, which leverages Large Language Models (LLM) to offer a unified generation system capable of seamlessly accommodating various types of prompts and integrating domain-expert models. DiffusionGPT constructs domain-specific Trees for various generative models based on prior knowledge. When provided with an input, the LLM parses the prompt and employs the Trees-of-Thought to guide the selection of an appropriate model, thereby relaxing input constraints and ensuring exceptional performance across diverse domains. Moreover, we introduce Advantage Databases, where the Tree-of-Thought is enriched with human feedback, aligning the model selection process with human preferences. Through extensive experiments and comparisons, we demonstrate the effectiveness of DiffusionGPT, showcasing its potential for pushing the boundaries of image synthesis in diverse domains. |
|
2024-01-19T00:00:00 | 2401.09865 | Improving fine-grained understanding in image-text pre-training | [
"Ioana Bica",
"Anastasija Ilić",
"Matthias Bauer",
"Goker Erdogan",
"Matko Bošnjak",
"Christos Kaplanis",
"Alexey A. Gritsenko",
"Matthias Minderer",
"Charles Blundell",
"Razvan Pascanu",
"Jovana Mitrović"
]
| We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations from image-text pairs. Given that multiple image patches often correspond to single words, we propose to learn a grouping of image patches for every token in the caption. To achieve this, we use a sparse similarity metric between image patches and language tokens and compute for each token a language-grouped vision embedding as the weighted average of patches. The token and language-grouped vision embeddings are then contrasted through a fine-grained sequence-wise loss that only depends on individual samples and does not require other batch samples as negatives. This enables more detailed information to be learned in a computationally inexpensive manner. SPARC combines this fine-grained loss with a contrastive loss between global image and text embeddings to learn representations that simultaneously encode global and local information. We thoroughly evaluate our proposed method and show improved performance over competing approaches both on image-level tasks relying on coarse-grained information, e.g. classification, as well as region-level tasks relying on fine-grained information, e.g. retrieval, object detection, and segmentation. Moreover, SPARC improves model faithfulness and captioning in foundational vision-language models. |
|
2024-01-19T00:00:00 | 2401.10225 | ChatQA: Building GPT-4 Level Conversational QA Models | [
"Zihan Liu",
"Wei Ping",
"Rajarshi Roy",
"Peng Xu",
"Mohammad Shoeybi",
"Bryan Catanzaro"
]
| In this work, we introduce ChatQA, a family of conversational question answering (QA) models, that obtain GPT-4 level accuracies. Specifically, we propose a two-stage instruction tuning method that can significantly improve the zero-shot conversational QA results from large language models (LLMs). To handle retrieval in conversational QA, we fine-tune a dense retriever on a multi-turn QA dataset, which provides comparable results to using the state-of-the-art query rewriting model while largely reducing deployment cost. Notably, our ChatQA-70B can outperform GPT-4 in terms of average score on 10 conversational QA datasets (54.14 vs. 53.90), without relying on any synthetic data from OpenAI GPT models. |
|
2024-01-19T00:00:00 | 2401.10166 | VMamba: Visual State Space Model | [
"Yue Liu",
"Yunjie Tian",
"Yuzhong Zhao",
"Hongtian Yu",
"Lingxi Xie",
"Yaowei Wang",
"Qixiang Ye",
"Yunfan Liu"
]
| https://github.com/MzeroMiko/VMamba | Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) stand as the two most popular foundation models for visual representation learning. While CNNs exhibit remarkable scalability with linear complexity w.r.t. image resolution, ViTs surpass them in fitting capabilities despite contending with quadratic complexity. A closer inspection reveals that ViTs achieve superior visual modeling performance through the incorporation of global receptive fields and dynamic weights. This observation motivates us to propose a novel architecture that inherits these components while enhancing computational efficiency. To this end, we draw inspiration from the recently introduced state space model and propose the Visual State Space Model (VMamba), which achieves linear complexity without sacrificing global receptive fields. To address the encountered direction-sensitive issue, we introduce the Cross-Scan Module (CSM) to traverse the spatial domain and convert any non-causal visual image into order patch sequences. Extensive experimental results substantiate that VMamba not only demonstrates promising capabilities across various visual perception tasks, but also exhibits more pronounced advantages over established benchmarks as the image resolution increases. Source code has been available at https://github.com/MzeroMiko/VMamba. |
2024-01-19T00:00:00 | 2401.10032 | FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder | [
"Tan Dat Nguyen",
"Ji-Hoon Kim",
"Youngjoon Jang",
"Jaehun Kim",
"Joon Son Chung"
]
| The goal of this paper is to generate realistic audio with a lightweight and fast diffusion-based vocoder, named FreGrad. Our framework consists of the following three key components: (1) We employ discrete wavelet transform that decomposes a complicated waveform into sub-band wavelets, which helps FreGrad to operate on a simple and concise feature space, (2) We design a frequency-aware dilated convolution that elevates frequency awareness, resulting in generating speech with accurate frequency information, and (3) We introduce a bag of tricks that boosts the generation quality of the proposed model. In our experiments, FreGrad achieves 3.7 times faster training time and 2.2 times faster inference speed compared to our baseline while reducing the model size by 0.6 times (only 1.78M parameters) without sacrificing the output quality. Audio samples are available at: https://mm.kaist.ac.kr/projects/FreGrad. |
|
2024-01-19T00:00:00 | 2401.09603 | Rethinking FID: Towards a Better Evaluation Metric for Image Generation | [
"Sadeep Jayasumana",
"Srikumar Ramalingam",
"Andreas Veit",
"Daniel Glasner",
"Ayan Chakrabarti",
"Sanjiv Kumar"
]
| As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID). FID estimates the distance between a distribution of Inception-v3 features of real images, and those of images generated by the algorithm. We highlight important drawbacks of FID: Inception's poor representation of the rich and varied content generated by modern text-to-image models, incorrect normality assumptions, and poor sample complexity. We call for a reevaluation of FID's use as the primary quality metric for generated images. We empirically demonstrate that FID contradicts human raters, it does not reflect gradual improvement of iterative text-to-image models, it does not capture distortion levels, and that it produces inconsistent results when varying the sample size. We also propose an alternative new metric, CMMD, based on richer CLIP embeddings and the maximum mean discrepancy distance with the Gaussian RBF kernel. It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient. Through extensive experiments and analysis, we demonstrate that FID-based evaluations of text-to-image models may be unreliable, and that CMMD offers a more robust and reliable assessment of image quality. |
|
2024-01-19T00:00:00 | 2401.10171 | SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild | [
"Andreas Engelhardt",
"Amit Raj",
"Mark Boss",
"Yunzhi Zhang",
"Abhishek Kar",
"Yuanzhen Li",
"Deqing Sun",
"Ricardo Martin Brualla",
"Jonathan T. Barron",
"Hendrik P. A. Lensch",
"Varun Jampani"
]
| We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background. Inverse rendering of an object based on unconstrained image collections is a long-standing challenge in computer vision and graphics and requires a joint optimization over shape, radiance, and pose. We show that an implicit shape representation based on a multi-resolution hash encoding enables faster and robust shape reconstruction with joint camera alignment optimization that outperforms prior work. Further, to enable the editing of illumination and object reflectance (i.e. material) we jointly optimize BRDF and illumination together with the object's shape. Our method is class-agnostic and works on in-the-wild image collections of objects to produce relightable 3D assets for several use cases such as AR/VR, movies, games, etc. Project page: https://shinobi.aengelhardt.com Video: https://www.youtube.com/watch?v=iFENQ6AcYd8&feature=youtu.be |
|
2024-01-22T00:00:00 | 2401.10774 | Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads | [
"Tianle Cai",
"Yuhong Li",
"Zhengyang Geng",
"Hongwu Peng",
"Jason D. Lee",
"Deming Chen",
"Tri Dao"
]
| The inference process in Large Language Models (LLMs) is often limited due to the absence of parallelism in the auto-regressive decoding process, resulting in most operations being restricted by the memory bandwidth of accelerators. While methods such as speculative decoding have been suggested to address this issue, their implementation is impeded by the challenges associated with acquiring and maintaining a separate draft model. In this paper, we present Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, Medusa constructs multiple candidate continuations and verifies them simultaneously in each decoding step. By leveraging parallel processing, Medusa introduces only minimal overhead in terms of single-step latency while substantially reducing the number of decoding steps required. We present two levels of fine-tuning procedures for Medusa to meet the needs of different use cases: Medusa-1: Medusa is directly fine-tuned on top of a frozen backbone LLM, enabling lossless inference acceleration. Medusa-2: Medusa is fine-tuned together with the backbone LLM, enabling better prediction accuracy of Medusa heads and higher speedup but needing a special training recipe that preserves the backbone model's capabilities. Moreover, we propose several extensions that improve or expand the utility of Medusa, including a self-distillation to handle situations where no training data is available and a typical acceptance scheme to boost the acceptance rate while maintaining generation quality. We evaluate Medusa on models of various sizes and training procedures. Our experiments demonstrate that Medusa-1 can achieve over 2.2x speedup without compromising generation quality, while Medusa-2 further improves the speedup to 2.3-3.6x. |
|
2024-01-22T00:00:00 | 2401.10822 | ActAnywhere: Subject-Aware Video Background Generation | [
"Boxiao Pan",
"Zhan Xu",
"Chun-Hao Paul Huang",
"Krishna Kumar Singh",
"Yang Zhou",
"Leonidas J. Guibas",
"Jimei Yang"
]
| Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community. This task involves synthesizing background that aligns with the motion and appearance of the foreground subject, while also complies with the artist's creative intention. We introduce ActAnywhere, a generative model that automates this process which traditionally requires tedious manual efforts. Our model leverages the power of large-scale video diffusion models, and is specifically tailored for this task. ActAnywhere takes a sequence of foreground subject segmentation as input and an image that describes the desired scene as condition, to produce a coherent video with realistic foreground-background interactions while adhering to the condition frame. We train our model on a large-scale dataset of human-scene interaction videos. Extensive evaluations demonstrate the superior performance of our model, significantly outperforming baselines. Moreover, we show that ActAnywhere generalizes to diverse out-of-distribution samples, including non-human subjects. Please visit our project webpage at https://actanywhere.github.io. |
|
2024-01-22T00:00:00 | 2401.10404 | Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution | [
"Xin Yuan",
"Jinoo Baek",
"Keyang Xu",
"Omer Tov",
"Hongliang Fei"
]
| We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capture spatial information for video generation. To accomplish this goal, we design an efficient architecture by inflating the weightings of the text-to-image SR model into our video generation framework. Additionally, we incorporate a temporal adapter to ensure temporal coherence across video frames. We investigate different tuning approaches based on our inflated architecture and report trade-offs between computational costs and super-resolution quality. Empirical evaluation, both quantitative and qualitative, on the Shutterstock video dataset, demonstrates that our approach is able to perform text-to-video SR generation with good visual quality and temporal consistency. To evaluate temporal coherence, we also present visualizations in video format in https://drive.google.com/drive/folders/1YVc-KMSJqOrEUdQWVaI-Yfu8Vsfu_1aO?usp=sharing . |
|
2024-01-22T00:00:00 | 2401.10891 | Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data | [
"Lihe Yang",
"Bingyi Kang",
"Zilong Huang",
"Xiaogang Xu",
"Jiashi Feng",
"Hengshuang Zhao"
]
| https://github.com/LiheYoung/Depth-Anything | This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, we aim to build a simple yet powerful foundation model dealing with any images under any circumstances. To this end, we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data (~62M), which significantly enlarges the data coverage and thus is able to reduce the generalization error. We investigate two simple yet effective strategies that make data scaling-up promising. First, a more challenging optimization target is created by leveraging data augmentation tools. It compels the model to actively seek extra visual knowledge and acquire robust representations. Second, an auxiliary supervision is developed to enforce the model to inherit rich semantic priors from pre-trained encoders. We evaluate its zero-shot capabilities extensively, including six public datasets and randomly captured photos. It demonstrates impressive generalization ability. Further, through fine-tuning it with metric depth information from NYUv2 and KITTI, new SOTAs are set. Our better depth model also results in a better depth-conditioned ControlNet. Our models are released at https://github.com/LiheYoung/Depth-Anything. |
2024-01-22T00:00:00 | 2401.10889 | Synthesizing Moving People with 3D Control | [
"Boyi Li",
"Jathushan Rajasegaran",
"Yossi Gandelsman",
"Alexei A. Efros",
"Jitendra Malik"
]
| In this paper, we present a diffusion model-based framework for animating people from a single image for a given target 3D motion sequence. Our approach has two core components: a) learning priors about invisible parts of the human body and clothing, and b) rendering novel body poses with proper clothing and texture. For the first part, we learn an in-filling diffusion model to hallucinate unseen parts of a person given a single image. We train this model on texture map space, which makes it more sample-efficient since it is invariant to pose and viewpoint. Second, we develop a diffusion-based rendering pipeline, which is controlled by 3D human poses. This produces realistic renderings of novel poses of the person, including clothing, hair, and plausible in-filling of unseen regions. This disentangled approach allows our method to generate a sequence of images that are faithful to the target motion in the 3D pose and, to the input image in terms of visual similarity. In addition to that, the 3D control allows various synthetic camera trajectories to render a person. Our experiments show that our method is resilient in generating prolonged motions and varied challenging and complex poses compared to prior methods. Please check our website for more details: https://boyiliee.github.io/3DHM.github.io/. |
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