title: MetaLadder: Ascending Mathematical Solution Quality via
    Analogical-Problem Reasoning Transfer
summary: 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 analogous
  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. Our code and data
  has been released at https://github.com/LHL3341/MetaLadder.
paper_authors._id: 67dc1aeab91614cb077cbdf4
paper_authors.name: Honglin Lin
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paper_authors.fullname: Honglin Lin
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paper_authors.user: LHL3341
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paper_authors.name: Zhuoshi Pan
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paper_authors.fullname: Zhuoshi Pan
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paper_authors.user: panzs
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paper_authors.name: Yu Li
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paper_authors.fullname: YU LI
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paper_authors.user: yu0226
paper_authors._id: 67dc1aeab91614cb077cbdf7
paper_authors.name: Qizhi Pei
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paper_authors.fullname: QizhiPei
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paper_authors._id: 67dc1aeab91614cb077cbdf8
paper_authors.name: Xin Gao
paper_authors._id: 67dc1aeab91614cb077cbdf9
paper_authors.name: Mengzhang Cai
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paper_authors.fullname: caimengzhang
paper_authors.type: user
paper_authors.user: caimz
paper_authors._id: 67dc1aeab91614cb077cbdfa
paper_authors.name: Conghui He
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/fa1f2ae7972d7cde99dab178136ccbb0.svg
paper_authors.fullname: Conghui He
paper_authors.type: user
paper_authors.user: conghui
paper_authors._id: 67dc1aeab91614cb077cbdfb
paper_authors.name: Lijun Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T19:32:13.107Z
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paper_authors.fullname: Lijun Wu
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paper_title: MetaLadder: Ascending Mathematical Solution Quality via
    Analogical-Problem Reasoning Transfer
paper_summary: 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 analogous
  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. Our code and data
  has been released at https://github.com/LHL3341/MetaLadder.
paper_ai_summary: MetaLadder enhances LLMs' mathematical reasoning by prompting them to recall analogous problems and restate the target question, significantly improving accuracy over standard methods.
paper_ai_keywords: Chain-of-Thought CoT MetaLadder meta-problems problem-restating mechanism reasoning transfer learning from examples generalization abilities