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Aeree Cho, Grace C. Kim, Alexander Karpekov +5 authors
Transformer Explainer is an interactive visualization tool that allows non-experts to understand the inner workings of the GPT-2 model through real-time experimentation and visualization in a web browser.
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Melisa Russak, Umar Jamil, Christopher Bryant +4 authors
Writing in the Margins (WiM) enhances Large Language Models' performance on long input sequences and retrieval tasks by using chunked prefill and marginal information, improving accuracy and F1-score without fine-tuning.
Hugo Laurençon, Andrés Marafioti, Victor Sanh +1 authors
A comprehensive tutorial on building vision-language models, detailing the development of Idefics3-8B with an improved dataset enhancing document understanding.
Dani Valevski, Yaniv Leviathan, Moab Arar +1 authors
GameNGen, a neural model-powered game engine, simulates high-quality gameplay in real-time using a diffusion model conditioned on past frames and actions.
Chris Lu, Cong Lu, Robert Tjarko Lange +3 authors
The AI Scientist is a framework that enables independent scientific research through automatic idea generation, experimentation, and paper writing using large language models.
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu +15 authors
Segment Anything Model 2 (SAM 2) uses a transformer architecture with streaming memory to achieve high performance in image and video segmentation, requiring fewer interactions and faster processing than previous models.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey +530 authors
Llama 3, a multilingual and multi-modal language model with 405B parameters, achieves competitive performance across tasks including image, video, and speech recognition when integrated through a compositional approach.
Le Xue, Manli Shu, Anas Awadalla +24 authors
xGen-MM, an extension of Salesforce's xGen initiative, provides a framework for developing Large Multimodal Models with pre-trained, instruction-tuned, and safety-tuned variants.
Yuan Yao, Tianyu Yu, Ao Zhang +20 authors
MiniCPM-V presents a series of efficient Multimodal Large Language Models optimized for end-side deployment, offering high performance and practical usability compared to larger models.
Shijia Yang, Bohan Zhai, Quanzeng You +3 authors
Correlation between cross-modal alignment and vision representation improves performance in multimodal large language models, enabling identification and training of optimal vision representation with reduced computational cost.
Rawal Khirodkar, Timur Bagautdinov, Julieta Martinez +5 authors
Sapiens, a family of vision models for human-centric tasks, achieves superior performance with self-supervised pretraining and can be easily fine-tuned for 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction.
Min Shi, Fuxiao Liu, Shihao Wang +12 authors
Mixture of vision encoders and resolutions in multimodal large language models improves performance through concatenation of visual tokens and a Pre-Alignment mechanism, leading to superior results on benchmarks.
Pengcheng Chen, Jin Ye, Guoan Wang +15 authors
GMAI-MMBench is a comprehensive general medical AI benchmark that evaluates large vision-language models across multiple medical domains and perceptual granularities, identifying key insufficiencies and highlighting areas for improvement.
Gemma Team, Morgane Riviere, Shreya Pathak +193 authors
Gemma 2 introduces improvements in the Transformer architecture through interleaving local-global attentions and group-query attention, showcasing superior performance relative to its size.
Zhenting Qi, Mingyuan Ma, Jiahang Xu +3 authors
rStar enhances small language models' reasoning capabilities through a self-play mutual generation-discrimination process without fine-tuning, improving accuracy across various reasoning tasks.
Yushi Bai, Jiajie Zhang, Xin Lv +6 authors
AgentWrite enables long context LLMs to generate outputs exceeding 20,000 words by decomposing tasks into subtasks, using a new dataset to train models with extended output capabilities.
Charlie Snell, Jaehoon Lee, Kelvin Xu +1 authors
Research on scaling test-time computation in LLMs finds that compute-optimal strategies can enhance performance and efficiency beyond larger base models.
Xun Liang, Hanyu Wang, Yezhaohui Wang +8 authors
Controllable Text Generation techniques for Large Language Models ensure predefined control conditions and high-quality text output, covering content and attribute control through various methods like retraining, fine-tuning, and latent manipulation.
Qianqian Xie, Dong Li, Mengxi Xiao +36 authors
Open-FinLLMs, a series of financial LLMs, enhance performance through comprehensive pre-training and instruction fine-tuning, demonstrating superior results in both zero-shot and few-shot evaluations, and excels in understanding tables and charts.
Chunting Zhou, Lili Yu, Arun Babu +7 authors
Transfusion combines language modeling and diffusion training to develop a multi-modal transformer model that scales effectively across text and image data, matching the performance of specialized models.
Huajian Xin, Z. Z. Ren, Junxiao Song +14 authors
DeepSeek-Prover-V1.5 improves theorem proving by optimizing training and inference, utilizing reinforcement learning, and proposing RMaxTS for diverse proof paths, achieving state-of-the-art results on miniF2F and ProofNet benchmarks.
Trung Dao, Thuan Hoang Nguyen, Thanh Le +4 authors
Enhancements to SwiftBrush through improved weight initialization, efficient LoRA training, and novel clamped CLIP loss achieve state-of-the-art image quality while maintaining generative diversity.
Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi +6 authors
Models Llama 3.1 8B and Mistral NeMo 12B are compressed using pruning and distillation, achieving state-of-the-art performance in a reduced parameter setting.
Bo Li, Yuanhan Zhang, Dong Guo +7 authors
LLaVA-OneVision is a unified multimodal model that advances performance across single-image, multi-image, and video scenarios with strong transfer learning capabilities.
Ryan Teknium, Jeffrey Quesnelle, Chen Guang
Hermes 3, a neutrally-aligned instruct and tool use model with strong reasoning and creative capabilities, achieves top performance on public benchmarks.
Fanqing Meng, Jin Wang, Chuanhao Li +9 authors
The Multimodal Multi-image Understanding (MMIU) benchmark assesses LVLMs across various multi-image tasks, highlighting challenges in spatial understanding.
Wenyi Hong, Weihan Wang, Ming Ding +22 authors
The CogVLM2 family of visual language models, including CogVLM2, CogVLM2-Video, and GLM-4V, advances image and video understanding with enhanced architectures and training methods.
Hyeongmin Lee, Jin-Young Kim, Kyungjune Baek +18 authors
The study introduces TWLV-I, a novel video foundation model that outperforms existing models in video comprehension by constructing robust representations for both motion and appearance.
Guosheng Dong, Da Pan, Yiding Sun +17 authors
A data processing pipeline and open-sourced details for training a large language model achieve competitive performance with commercial models.
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