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。
Dawei Gao, Zitao Li, Yuexiang Xie +20 authors
AgentScope enhances agentic applications by providing flexible tool-based interactions, unified interfaces, and advanced infrastructure based on the ReAct paradigm, supporting efficient and safe development and deployment.
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.
Hao Sun, Zile Qiao, Jiayan Guo +6 authors
ZeroSearch is a reinforcement learning framework that improves LLM search capabilities without using real search engines, using supervised fine-tuning and curriculum-based rollouts to handle document quality and API cost challenges.
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.
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.
Yiran Zhao, Chaoqun Liu, Yue Deng +8 authors
Babel is an open multilingual LLM that expands its parameter count through layer extension, covering numerous languages and achieving superior performance in multilingual tasks compared to other open LLMs.
Zeyi Sun, Ziyang Chu, Pan Zhang +6 authors
X-Prompt, an auto-regressive large-vision language model, uses in-context learning to achieve competitive performance in seen and unseen image generation tasks.
Ming Li, Yanhong Li, Tianyi Zhou
Investigating gradient patterns in LLMs during fast vs. slow training reveals that slow thinking (chain-of-thoughts) enhances learning stability and distinguishes correct reasoning paths.
Nikhil Kandpal, Brian Lester, Colin Raffel +24 authors
The Common Pile v0.1 dataset, an 8-terabyte collection of openly licensed text, is used to train competitive 7 billion parameter LLMs.
Zafeirios Fountas, Martin A Benfeghoul, Adnan Oomerjee +4 authors
EM-LLM enhances LLMs by integrating human episodic memory principles, enabling efficient handling of extensive contexts through coherent event segmentation and retrieval.
Maosong Cao, Alexander Lam, Haodong Duan +3 authors
CompassJudger-1 is an open-source, all-in-one judge LLM that evaluates LLMs through unitary scoring, model comparisons, critiques, and various tasks, supported by the JudgerBench benchmark.
Tianle Cai, Yuhong Li, Zhengyang Geng +4 authors
Medusa enhances Large Language Model inference by adding parallel decoding heads and using tree-based attention to predict multiple tokens simultaneously, achieving significant speedup with minimal latency.
Zichen Liu, Changyu Chen, Wenjun Li +5 authors
A study of R1-Zero training identifies pretraining effects on RL performance and proffers Dr. GRPO to enhance token efficiency, achieving superior accuracy on AIME 2024.
Liang Chen, Zekun Wang, Shuhuai Ren +24 authors
A comprehensive taxonomy unifies multimodal understanding and generation through Next Token Prediction, covering aspects like tokenization, model architectures, task representation, datasets, and open challenges.
Yafu Li, Xuyang Hu, Xiaoye Qu +2 authors
TPO refines LLM outputs in real-time using textual critiques, achieving preference alignment without retraining.
Junlin Wang, Jue Wang, Ben Athiwaratkun +2 authors
A Mixture-of-Agents (MoA) methodology combines multiple large language models (LLMs) to achieve state-of-the-art performance on various evaluations.
Ruibin Yuan, Hanfeng Lin, Yi Wang +32 authors
ChatMusician, an open-source LLM with intrinsic musical abilities, outperforms GPT-4 in music generation and understanding tasks using text-compatible music notation.
Junyou Li, Qin Zhang, Yangbin Yu +2 authors
A sampling-and-voting method enhances large language models' performance by increasing the number of agents, with effectiveness tied to task difficulty.
Zorik Gekhman, Eyal Ben David, Hadas Orgad +5 authors
LLMs encode more internal factual knowledge than they express externally, with some knowledge so deeply hidden that it is never generated, despite repeated sampling.
Chaofan Tao, Qian Liu, Longxu Dou +5 authors
Investigating the impact of vocabulary size on the scaling of large language models reveals that larger vocabularies improve performance when considering compute budgets, and current models often use suboptimal vocabulary sizes.
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