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。
Ting Jiang, Shaohan Huang, Shengyue Luo +8 authors
MoRA, a high-rank updating method using square matrices, enhances the ability of large language models to learn and memorize new knowledge, especially in memory-intensive tasks, compared to LoRA.
Zimu Lu, Aojun Zhou, Ke Wang +5 authors
A novel method for generating mathematical code with reasoning steps enhances mathematical reasoning abilities in large language models using a comprehensive dataset named MathCode-Pile.
Weihao Zeng, Yuzhen Huang, Lulu Zhao +3 authors
B-STaR framework autonomously adjusts configurations to enhance exploration and exploitation, improving performance in self-improving models.
Bingbin Liu, Sebastien Bubeck, Ronen Eldan +5 authors
A synthetic dataset of math problems paired with solutions enables small language models to achieve high accuracy in mathematical reasoning, outperforming larger models.
Hugh Zhang, Jeff Da, Dean Lee +12 authors
Evaluation of large language models on a new math benchmark, GSM1k, reveals that many models exhibit signs of overfitting to the existing GSM8k benchmark, leading to performance drops on GSM1k.
Zheng Yuan, Hongyi Yuan, Chengpeng Li +3 authors
Rejection sampling fine-tuning enhances mathematical reasoning in large language models by generating auxiliary datasets of correct reasoning paths, leading to higher performance and better scalability compared to supervised fine-tuning.
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