Problem-Solving Logic Guided Curriculum In-Context Learning for LLMs Complex Reasoning
Xuetao Ma, Wenbin Jiang, Hua Huang

TL;DR
This paper introduces a problem-solving logic guided curriculum in-context learning method that improves large language models' complex reasoning by selecting and ordering examples based on problem-solving logic and difficulty, outperforming previous approaches.
Contribution
The study proposes a novel curriculum ICL strategy guided by problem-solving logic, including a fine-tuned model and a new instruction set for better example selection and ordering.
Findings
Outperforms previous ICL methods in benchmarks
Enhances reasoning capabilities of LLMs
Improves efficiency of in-context learning
Abstract
In-context learning (ICL) can significantly enhance the complex reasoning capabilities of large language models (LLMs), with the key lying in the selection and ordering of demonstration examples. Previous methods typically relied on simple features to measure the relevance between examples. We argue that these features are not sufficient to reflect the intrinsic connections between examples. In this study, we propose a curriculum ICL strategy guided by problem-solving logic. We select demonstration examples by analyzing the problem-solving logic and order them based on curriculum learning. Specifically, we constructed a problem-solving logic instruction set based on the BREAK dataset and fine-tuned a language model to analyze the problem-solving logic of examples. Subsequently, we selected appropriate demonstration examples based on problem-solving logic and assessed their difficulty…
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Taxonomy
MethodsSparse Evolutionary Training
