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KwaiYiiMath: Technical Report

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arxiv 2310.07488 v2 pith:XTTYRYNF submitted 2023-10-11 cs.CL cs.AIcs.LG

Jiayi Fu , Lei Lin , Xiaoyang Gao , Pengli Liu , Zhengzong Chen , Zhirui Yang , Shengnan Zhang , Xue Zheng
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Yan Li Yuliang Liu Xucheng Ye Yiqiao Liao Chao Liao Bin Chen Chengru Song Junchen Wan Zijia Lin Fuzheng Zhang Zhongyuan Wang Di Zhang Kun Gai
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classification cs.CLcs.AIcs.LG
keywords kwaiyiimathmathematicalmodelstasksabilitieschinesekmathlanguage
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Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.

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