title: Beyond Examples: High-level Automated Reasoning Paradigm in In-Context
    Learning via MCTS
summary: In-context Learning (ICL) enables large language models (LLMs) to tackle
  downstream tasks through sophisticated prompting and high-quality
  demonstrations. However, this traditional ICL paradigm shows limitations when
  facing complex mathematical reasoning tasks, primarily due to its heavy
  dependence on example quality and the necessity for human intervention in
  challenging scenarios. To address these limitations, this paper presents
  HiAR-ICL, a High-level Automated Reasoning paradigm
  in ICL that shifts focus from specific examples to abstract thinking
  patterns, extending the conventional concept of context in ICL. HiAR-ICL
  introduces five atomic reasoning actions as fundamental components for
  constructing chain-structured patterns. Using Monte Carlo Tree Search, we
  explore reasoning paths and construct thought cards to guide subsequent
  inference. We then develop a cognitive complexity framework that dynamically
  matches problems with appropriate thought cards. Experimental results
  demonstrate HiAR-ICL's effectiveness, achieving state-of-the-art accuracy
  (79.6%) on the MATH benchmark with Qwen2.5-7B-Instruct, surpassing GPT-4o
  (76.6%) and Claude 3.5 (71.1%).
paper_authors._id: 674b4af16421c587617a966f
paper_authors.name: Jinyang Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T08:50:22.252Z
paper_authors._id: 6747de57f8cab58c22ec94a2
paper_authors.avatarUrl: /avatars/5bae0341862fac24564781c0fa32aac5.svg
paper_authors.fullname: Jinyang Wu
paper_authors.type: user
paper_authors.user: Jinyang23
paper_authors._id: 674b4af16421c587617a9670
paper_authors.name: Mingkuan Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-02T09:16:15.000Z
paper_authors._id: 660d13b85e00095e45ee28e0
paper_authors.avatarUrl: /avatars/8f06c01edc2a791266feadc775acb901.svg
paper_authors.fullname: FengMingkuan
paper_authors.type: user
paper_authors.user: fmk345
paper_authors._id: 674b4af16421c587617a9671
paper_authors.name: Shuai Zhang
paper_authors._id: 674b4af16421c587617a9672
paper_authors.name: Feihu Che
paper_authors._id: 674b4af16421c587617a9673
paper_authors.name: Zengqi Wen
paper_authors._id: 674b4af16421c587617a9674
paper_authors.name: Jianhua Tao
paper_title: Beyond Examples: High-level Automated Reasoning Paradigm in In-Context
    Learning via MCTS
paper_summary: In-context Learning (ICL) enables large language models (LLMs) to tackle
  downstream tasks through sophisticated prompting and high-quality
  demonstrations. However, this traditional ICL paradigm shows limitations when
  facing complex mathematical reasoning tasks, primarily due to its heavy
  dependence on example quality and the necessity for human intervention in
  challenging scenarios. To address these limitations, this paper presents
  HiAR-ICL, a High-level Automated Reasoning paradigm
  in ICL that shifts focus from specific examples to abstract thinking
  patterns, extending the conventional concept of context in ICL. HiAR-ICL
  introduces five atomic reasoning actions as fundamental components for
  constructing chain-structured patterns. Using Monte Carlo Tree Search, we
  explore reasoning paths and construct thought cards to guide subsequent
  inference. We then develop a cognitive complexity framework that dynamically
  matches problems with appropriate thought cards. Experimental results
  demonstrate HiAR-ICL's effectiveness, achieving state-of-the-art accuracy
  (79.6%) on the MATH benchmark with Qwen2.5-7B-Instruct, surpassing GPT-4o
  (76.6%) and Claude 3.5 (71.1%).
paper_ai_keywords: In-context Learning (ICL) Large language models (LLMs) Prompting High-quality demonstrations Mathematical reasoning tasks High-level Automated Reasoning (HiAR-ICL) Abstract thinking patterns Context Atomic reasoning actions Behaviors components Chain-structured patterns Monte Carlo Tree Search Reasoning paths Thought cards Cognitive complexity information MATH benchmark Qwen2.5-7B-Instruct GPT-4o Claude 3.5