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From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision
March 22, 2024, 4:48 a.m. | Qingwen Lin, Boyan Xu, Zhengting Huang, Ruichu Cai
cs.CL updates on arXiv.org arxiv.org
Abstract: Addressing the challenge of high annotation costs in solving Math Word Problems (MWPs) through full supervision with intermediate equations, recent works have proposed weakly supervised task settings that rely solely on the final answer as a supervised signal. Existing leading approaches typically employ various search techniques to infer intermediate equations, but cannot ensure their semantic consistency with natural language descriptions. The rise of Large Language Models (LLMs) like ChatGPT has opened up new possibilities for …
abstract annotation arxiv challenge costs cs.cl expertise intermediate math signal supervision through type word
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