Beyond Examples: High-level Automated Reasoning Paradigm in In-Context
  Learning via MCTS
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%).
674b4af16421c587617a966f
Jinyang Wu
claimed_verified
2024-12-02T08:50:22.252Z
6747de57f8cab58c22ec94a2
/avatars/5bae0341862fac24564781c0fa32aac5.svg
Jinyang Wu
user
Jinyang23
674b4af16421c587617a9670
Mingkuan Feng
admin_assigned
2024-12-02T09:16:15.000Z
660d13b85e00095e45ee28e0
/avatars/8f06c01edc2a791266feadc775acb901.svg
FengMingkuan
user
fmk345
674b4af16421c587617a9671
Shuai Zhang
674b4af16421c587617a9672
Feihu Che
674b4af16421c587617a9673
Zengqi Wen
674b4af16421c587617a9674
Jianhua Tao
Beyond Examples: High-level Automated Reasoning Paradigm in In-Context
  Learning via MCTS
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%).
HiAR-ICL, a high-level automated reasoning paradigm in In-context Learning (ICL), uses atomic reasoning actions, Monte Carlo Tree Search, and thought cards to improve mathematical reasoning accuracy compared to existing models.
In-context Learning ICL HiAR-ICL atomic reasoning actions Monte Carlo Tree Search thought cards cognitive complexity framework MATH benchmark Qwen2.5-7B-Instruct GPT-4 Claude 3.5