no code implementations • 23 Feb 2024 • Yezeng Chen, Zui Chen, Yi Zhou
Although large language models demonstrate emergent abilities in solving math word problems, there is a challenging task in complex multi-step mathematical reasoning tasks.
1 code implementation • 23 Feb 2024 • Zui Chen, Yezeng Chen, Jiaqi Han, Zhijie Huang, Ji Qi, Yi Zhou
Large language models (LLMs) are displaying emergent abilities for math reasoning tasks, and there is a growing attention on enhancing the ability of open-source LLMs through supervised fine-tuning (SFT). In this paper, we aim to explore a general data strategy for supervised data to help optimize and expand math reasoning ability. Firstly, we determine the ability boundary of reasoning paths augmentation by identifying these paths' minimal optimal set. Secondly, we validate that different abilities of the model can be cumulatively enhanced by Mix of Minimal Optimal Sets of corresponding types of data, while our models MMOS achieve SOTA performance on series base models under much lower construction costs. Besides, we point out GSM-HARD is not really hard and today's LLMs no longer lack numerical robustness. Also, we provide an Auto Problem Generator for robustness testing and educational applications. Our code and data are publicly available at https://github. com/cyzhh/MMOS.
Ranked #2 on Math Word Problem Solving on ASDiv-A (using extra training data)
1 code implementation • 9 Nov 2023 • HaoYi Wu, Wenyang Hui, Yezeng Chen, Weiqi Wu, Kewei Tu, Yi Zhou
Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has.