Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Jian Yang, Xianglong Liu, Weifeng Lv +68 authors
A comprehensive guide to code LLMs, covering their lifecycle from data curation to deployment, including techniques, trade-offs, and research-practice gaps.
Grégoire Mialon, Clémentine Fourrier, Craig Swift +3 authors
GAIA benchmarks general AI assistants using real-world questions that challenge both reasoning and multi-modality handling, showcasing a significant gap between human and AI performance.
Khalil Hennara, Muhammad Hreden, Mohamed Motaism Hamed +3 authors
Mutarjim is a compact Arabic-English translation model that outperforms larger models on established benchmarks and achieves state-of-the-art performance on a new comprehensive Tarjama-25 benchmark.
Zhe Chen, Weiyun Wang, Yue Cao +37 authors
InternVL 2.5, an advanced multimodal large language model, showcases competitive performance across various benchmarks, including multimodal reasoning and understanding, and is the first open-source model to surpass 70% on the MMMU benchmark using Chain-of-Thought reasoning.
Marah Abdin, Jyoti Aneja, Harkirat Behl +24 authors
A 14-billion parameter language model surpasses its teacher model in STEM-focused QA through strategic use of synthetic data, improved data quality, and enhanced training techniques.
Seungone Kim, Juyoung Suk, Shayne Longpre +7 authors
Prometheus 2 is an advanced open-source evaluator LM that aligns closely with human and proprietary LM judgments, supporting both direct assessment and pairwise ranking with customizable criteria.
Justin Zhao, Timothy Wang, Wael Abid +7 authors
LoRA fine-tuning reduces computational resources while maintaining high performance across various tasks and models, and LoRAX allows for efficient deployment of multiple LLMs on a single GPU.
Wei Pang, Kevin Qinghong Lin, Xiangru Jian +2 authors
A benchmark and metric suite for poster generation evaluates visual quality, coherence, and content accuracy, leading to a multi-agent pipeline that outperforms existing models with reduced computational cost.
Alexey Gorbatovski, Boris Shaposhnikov, Alexey Malakhov +5 authors
A new method, Trust Region DPO (TR-DPO), is proposed to improve policy alignment in reinforcement learning, outperforming Direct Preference Optimization (DPO) by up to 19% on key datasets by updating the reference policy during training.
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.
Peng Wang, Shuai Bai, Sinan Tan +16 authors
The Qwen2-VL Series uses Naive Dynamic Resolution and Multimodal Rotary Position Embedding to enhance visual processing and achieves competitive performance on multimodal benchmarks.
Zhenyi Liao, Qingsong Xie, Yanhao Zhang +4 authors
The study enhances visual-spatial reasoning in multi-modal large language models through GRPO training using the VSI-100k dataset, demonstrating significant performance improvements over base models.
Siheng Li, Cheng Yang, Zesen Cheng +4 authors
A novel method enables LLMs to self-improve in long-context reasoning through sampling, scoring with Minimum Bayes Risk, and supervised fine-tuning, outperforming expert-annotated data approaches.
Longwen Zhang, Qixuan Zhang, Haoran Jiang +4 authors
BANG is a generative approach using latent diffusion models and temporal attention to enable intuitive, part-level decomposition of 3D objects with precise control and multimodal interaction.
Wei Chen, Zhiyuan Li
A new on-device method improves the performance of a large language model, matching GPT-4's accuracy and latency while reducing context length and enhancing deployment on edge devices.
Santosh V. Patapati
A BiLSTM-based multi-modal model combines audio, facial, and text data using a GPT-4 model to classify depression, achieving state-of-the-art performance in binary classification tasks.
Yubo Wang, Xiang Yue, Wenhu Chen
Critique Fine-Tuning (CFT) improves language model performance by training them to critique noisy responses, showing better reasoning ability than standard Supervised Fine-Tuning (SFT).
Seungone Kim, Jamin Shin, Yejin Cho +8 authors
Prometheus, an open-source LLM, matches GPT-4's evaluation capabilities using customized score rubrics and outperforms ChatGPT across various benchmarks.
Xianjie Wu, Jian Yang, Linzheng Chai +10 authors
TableLLM, trained on TableInstruct, benchmarks performance in tabular data reasoning and highlights the need for improvement in real-world applications.
Liang Zeng, Liangjun Zhong, Liang Zhao +9 authors
Skywork-Math model series achieves high performance in math reasoning by fine-tuning on a large and diverse dataset, demonstrating that data quantity significantly enhances LLM mathematical capabilities.
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