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。
Chunting Zhou, Pengfei Liu, Puxin Xu +12 authors
A 65B parameter LLaMa language model trained with minimal instructional data matches or outperforms models with extensive human preference modeling in most cases, indicating that pretraining is predominantly responsible for knowledge acquisition.
Shuyan Zhou, Frank F. Xu, Hao Zhu +8 authors
WebArena, a realistic and reproducible environment, evaluates the performance of autonomous agents performing complex tasks on websites using benchmark tasks that require human-like reasoning.
Aojun Zhou, Ke Wang, Zimu Lu +8 authors
A novel code-based self-verification method significantly improves math reasoning in GPT-4 Code Interpreter, enhancing accuracy on the MATH dataset.
Lichang Chen, Shiyang Li, Jun Yan +8 authors
A data selection strategy using a strong LLM improves instruction-following models by filtering high-quality data, enhancing performance and reducing training time.
Tung Phung, Victor-Alexandru Pădurean, José Cambronero +5 authors
Systematic evaluation of ChatGPT and GPT-4 in educational scenarios demonstrates GPT-4's superior performance over ChatGPT and identifies areas needing improvement.
Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang +2 authors
GPT-4 demonstrates superior self-repair capabilities on APPS dataset compared to GPT-3.5, with performance boosted when feedback is provided by GPT-4 or human programmers.
Kaiyu Yang, Aidan M. Swope, Alex Gu +6 authors
LeanDojo provides open-source tools, data, and benchmarks to facilitate research on machine learning methods for theorem proving, introducing ReProver, an LLM-based prover that uses retrieval for premise selection.
Guanzhi Wang, Yuqi Xie, Yunfan Jiang +5 authors
Voyager is an LLM-powered agent in Minecraft that autonomously explores, learns, and discovers skills through a curriculum, skill library, and prompting mechanism, demonstrating superior lifelong learning and task-solving capabilities.
Jianyi Zhang, Saeed Vahidian, Martin Kuo +4 authors
Federated Instruction Tuning (FedIT) leverages federated learning to improve generative large language models with diverse local instructions while maintaining privacy.
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