MetaLadder: Ascending Mathematical Solution Quality via
  Analogical-Problem Reasoning Transfer
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.
67dc1aeab91614cb077cbdf4
Honglin Lin
admin_assigned
2025-03-20T19:31:18.591Z
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Honglin Lin
user
LHL3341
67dc1aeab91614cb077cbdf5
Zhuoshi Pan
67dc1aeab91614cb077cbdf6
Yu Li
claimed_verified
2025-04-29T07:59:58.967Z
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YU LI
user
yu0226
67dc1aeab91614cb077cbdf7
Qizhi Pei
admin_assigned
2025-03-20T19:31:39.436Z
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QizhiPei
user
QizhiPei
67dc1aeab91614cb077cbdf8
Xin Gao
67dc1aeab91614cb077cbdf9
Mengzhang Cai
admin_assigned
2025-03-20T19:31:50.091Z
677bc1cb02b4dd5d7b32747a
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caimengzhang
user
caimz
67dc1aeab91614cb077cbdfa
Conghui He
admin_assigned
2025-03-20T19:31:58.902Z
63f9fca8d4349b157a109eec
/avatars/fa1f2ae7972d7cde99dab178136ccbb0.svg
Conghui He
user
conghui
67dc1aeab91614cb077cbdfb
Lijun Wu
admin_assigned
2025-03-20T19:32:13.107Z
643e60d96db6ba8c5ee177ad
/avatars/73ac7740e462ba0b53a2f2480d9f1e3e.svg
Lijun Wu
user
apeters
MetaLadder: Ascending Mathematical Solution Quality via
  Analogical-Problem Reasoning Transfer
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.
MetaLadder enhances LLMs' mathematical reasoning by prompting them to recall analogous problems and restate the target question, significantly improving accuracy over standard methods.
Chain-of-Thought CoT MetaLadder meta-problems problem-restating mechanism reasoning transfer learning from examples generalization abilities