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。
Zhihong Shao, Yuxiang Luo, Chengda Lu +6 authors
A self-verifying large language model for theorem proving improves mathematical reasoning by incentivizing rigorous step-by-step derivations and achieves high scores in international competitions.
Junjie Li, Xinrui Guo, Yuhao Wu +3 authors
Large language models struggle with maintaining narrative consistency over long-form storytelling, exhibiting predictable patterns of contradictions that this benchmark identifies and categorizes.
Junnan Liu, Hongwei Liu, Linchen Xiao +6 authors
G-Pass@k and LiveMathBench assess Large Language Models' reasoning capabilities and consistency using complex mathematical problems.
Zifan Zheng, Yezhaohui Wang, Yuxin Huang +4 authors
This survey explores the interpretability and underlying mechanisms of attention heads within Large Language Models to understand their reasoning processes.
Yuxiang Ji, Ziyu Ma, Yong Wang +3 authors
Tree-based Group Relative Policy Optimization (Tree-GRPO) enhances reinforcement learning for large language models by using tree search to improve rollouts and estimate grouped relative advantages, outperforming chain-based methods.
Hongtao Wu, Boyun Zheng, Dingjie Song +5 authors
Medical AI Scientist represents the first autonomous research framework designed for clinical applications, enabling evidence-based hypothesis generation and manuscript drafting through clinician-engineer collaboration across three research modes.
Sergey Pletenev, Maria Marina, Daniil Moskovskiy +4 authors
Low-rank adaptation (LoRA) effectively updates LLMs with new knowledge but risks degrading performance on external benchmarks due to data bias and overconfidence.
Zichen Liu, Anya Sims, Keyu Duan +16 authors
GEM, an open-source environment simulator, facilitates experience-based learning for large language models by providing a standardized framework and diverse environments for training and benchmarking reinforcement learning algorithms.
Zonglin Yang, Lidong Bing
MOOSE-Star framework enables efficient training and inference for generative reasoning by addressing combinatorial complexity through decomposed subtasks, hierarchical search, and bounded composition.
Mingqi Wu, Zhihao Zhang, Qiaole Dong +9 authors
Research on enhancing reasoning capabilities in large language models using reinforcement learning reveals that accurate reward signals are crucial for performance improvement, and current benchmarks may be unreliable due to data contamination.
Yangning Li, Weizhi Zhang, Yuyao Yang +17 authors
This survey integrates reasoning and retrieval in Large Language Models to improve factuality and multi-step inference, highlighting Synergized RAG-Reasoning frameworks and outlining future research directions.
Junyu Luo, Xiao Luo, Kaize Ding +3 authors
A noise-robust supervised fine-tuning framework enhances large language models' performance in downstream tasks by detecting and relabeling noisy data.
Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai +5 authors
CulturaX is a large multilingual dataset designed to improve the training and quality of large language models through rigorous data cleaning and deduplication.
Yilei Jiang, Jinyuan Hu, Qianyin Xiao +8 authors
OpenGame is an open-source agentic framework for end-to-end web game creation that uses specialized code models and evaluation benchmarks to overcome challenges in interactive application development.
Eilam Shapira, Omer Madmon, Itamar Reinman +3 authors
A benchmark for evaluating Large Language Models in strategic, language-based games provides insights into their rationality, mimicry of human behavior, and impact of economic characteristics on performance and outcome.
Yuhao Zhang, Zhiheng Liu, Fan Bu +3 authors
Soundwave addresses the representation space gap and sequence length inconsistency in end-to-end speech large language models using an efficient training strategy and novel architecture, outperforming Qwen2-Audio with significantly less data.
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne +9 authors
Large language models demonstrate strong compression capabilities, outperforming domain-specific compressors and offering new insights into scaling laws, tokenization, and in-context learning.
Nex-AGI Team, Yuxuan Cai, Lu Chen +62 authors
The introduction of NexAU, NexA4A, and NexGAP enables the scaling of complexity, diversity, and fidelity in interactive environments for training large language models as autonomous agents, resulting in superior performance.
Tianyu Zheng, Ge Zhang, Tianhao Shen +5 authors
OpenCodeInterpreter, an open-source system for generating, executing, and refining code, achieves high performance on benchmarks through execution and human feedback, reducing the gap with proprietary systems like GPT-4 Code Interpreter.
Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri +1 authors
State-of-the-art large language models with vision capabilities perform poorly on simple visual tasks, indicating limitations in their visual understanding.
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