ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations

• URL: http://arxiv.org/abs/2506.03763v1
• Date: Wed, 04 Jun 2025 09:27:21 GMT
• Title: ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations
• Authors: Quang Hieu Pham, Thuy Duong Nguyen, Tung Pham, Anh Tuan Luu, Dat Quoc Nguyen,
• Abstract summary: We propose a new approach named ClozeMath to fine-tune large language models for mathematical reasoning.<n>Our ClozeMath involves a text-infilling task that predicts masked equations from a given solution, analogous to cloze exercises used in human learning.<n> Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses the strong baseline Masked Thought in performance and robustness.
• Score: 29.51572057789961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The capabilities of large language models (LLMs) have been enhanced by training on data that reflects human thought processes, such as the Chain-of-Thought format. However, evidence suggests that the conventional scheme of next-word prediction may not fully capture how humans learn to think. Inspired by how humans generalize mathematical reasoning, we propose a new approach named ClozeMath to fine-tune LLMs for mathematical reasoning. Our ClozeMath involves a text-infilling task that predicts masked equations from a given solution, analogous to cloze exercises used in human learning. Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses the strong baseline Masked Thought in performance and robustness, with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding. Additionally, we conduct an ablation study to analyze the effects of various architectural and implementation choices on our approach.

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