@inproceedings{lin-etal-2025-metaladder,
title = "{M}eta{L}adder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer",
author = "Lin, Honglin and
Pan, Zhuoshi and
Pei, Qizhi and
Gao, Xin and
Li, Yu and
Cai, Mengzhang and
He, Conghui and
Wu, Lijun",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.232/",
pages = "4328--4354",
ISBN = "979-8-89176-335-7",
abstract = "Large Language Models (LLMs) have demonstrated promising capabilities in solving mathematical reasoning tasks, leveraging Chain-of-Thought (CoT) data as a vital component in guiding answer generation. Current paradigms typically generate CoT and answers directly for a given problem, diverging from human problem-solving strategies to some extent. Humans often solve problems by recalling analogous cases and leveraging their solutions to reason about the current task. Inspired by this cognitive process, we propose \textbf{MetaLadder}, a novel framework that explicitly prompts LLMs to recall and reflect on meta-problems, those structurally or semantically analogical problems, alongside their CoT solutions before addressing the target problem. Additionally, we introduce a problem-restating mechanism to enhance the model{'}s comprehension of the target problem by regenerating the original question, which further improves reasoning accuracy. Therefore, the model can achieve reasoning transfer from analogical problems, mimicking human-like ``learning from examples'' and generalization abilities. Extensive experiments on mathematical benchmarks demonstrate that our MetaLadder significantly boosts LLMs' problem-solving accuracy, largely outperforming standard CoT-based methods (\textbf{10.3{\%}} accuracy gain) and other methods."
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<abstract>Large Language Models (LLMs) have demonstrated promising capabilities in solving mathematical reasoning tasks, leveraging Chain-of-Thought (CoT) data as a vital component in guiding answer generation. Current paradigms typically generate CoT and answers directly for a given problem, diverging from human problem-solving strategies to some extent. Humans often solve problems by recalling analogous cases and leveraging their solutions to reason about the current task. Inspired by this cognitive process, we propose MetaLadder, a novel framework that explicitly prompts LLMs to recall and reflect on meta-problems, those structurally or semantically analogical problems, alongside their CoT solutions before addressing the target problem. Additionally, we introduce a problem-restating mechanism to enhance the model’s comprehension of the target problem by regenerating the original question, which further improves reasoning accuracy. Therefore, the model can achieve reasoning transfer from analogical problems, mimicking human-like “learning from examples” and generalization abilities. Extensive experiments on mathematical benchmarks demonstrate that our MetaLadder significantly boosts LLMs’ problem-solving accuracy, largely outperforming standard CoT-based methods (10.3% accuracy gain) and other methods.</abstract>
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%0 Conference Proceedings
%T MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer
%A Lin, Honglin
%A Pan, Zhuoshi
%A Pei, Qizhi
%A Gao, Xin
%A Li, Yu
%A Cai, Mengzhang
%A He, Conghui
%A Wu, Lijun
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F lin-etal-2025-metaladder
%X Large Language Models (LLMs) have demonstrated promising capabilities in solving mathematical reasoning tasks, leveraging Chain-of-Thought (CoT) data as a vital component in guiding answer generation. Current paradigms typically generate CoT and answers directly for a given problem, diverging from human problem-solving strategies to some extent. Humans often solve problems by recalling analogous cases and leveraging their solutions to reason about the current task. Inspired by this cognitive process, we propose MetaLadder, a novel framework that explicitly prompts LLMs to recall and reflect on meta-problems, those structurally or semantically analogical problems, alongside their CoT solutions before addressing the target problem. Additionally, we introduce a problem-restating mechanism to enhance the model’s comprehension of the target problem by regenerating the original question, which further improves reasoning accuracy. Therefore, the model can achieve reasoning transfer from analogical problems, mimicking human-like “learning from examples” and generalization abilities. Extensive experiments on mathematical benchmarks demonstrate that our MetaLadder significantly boosts LLMs’ problem-solving accuracy, largely outperforming standard CoT-based methods (10.3% accuracy gain) and other methods.
%U https://aclanthology.org/2025.findings-emnlp.232/
%P 4328-4354
Markdown (Informal)
[MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer](https://aclanthology.org/2025.findings-emnlp.232/) (Lin et al., Findings 2025)
ACL
- Honglin Lin, Zhuoshi Pan, Qizhi Pei, Xin Gao, Yu Li, Mengzhang Cai, Conghui He, and Lijun Wu. 2025. MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4328–4354, Suzhou, China. Association for Computational Linguistics.