@inproceedings{pham-etal-2025-clozemath,
title = "{C}loze{M}ath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations",
author = "Pham, Quang Hieu and
Nguyen, Thuy Duong and
Pham, Tung and
Luu, Anh Tuan and
Nguyen, Dat Quoc",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.738/",
doi = "10.18653/v1/2025.findings-acl.738",
pages = "14322--14329",
ISBN = "979-8-89176-256-5",
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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%0 Conference Proceedings
%T ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations
%A Pham, Quang Hieu
%A Nguyen, Thuy Duong
%A Pham, Tung
%A Luu, Anh Tuan
%A Nguyen, Dat Quoc
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F pham-etal-2025-clozemath
%X 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.
%R 10.18653/v1/2025.findings-acl.738
%U https://aclanthology.org/2025.findings-acl.738/
%U https://doi.org/10.18653/v1/2025.findings-acl.738
%P 14322-14329
Markdown (Informal)
[ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations](https://aclanthology.org/2025.findings-acl.738/) (Pham et al., Findings 2025)
ACL