超越示例：基于MCTS的上层自动推理范式在上下文学习中

Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS

November 27, 2024

作者: Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zengqi Wen, Jianhua Tao

cs.AI

摘要

在上下文学习（ICL）中，大型语言模型（LLMs）通过复杂的提示和高质量的示范来处理下游任务。然而，传统的ICL范式在面对复杂的数学推理任务时存在局限性，主要是由于其对示例质量的严重依赖以及在挑战性场景中需要人类干预。为了解决这些限制，本文提出了HiAR-ICL，一种基于高级自动推理范式的ICL，将重点从具体示例转移到抽象思维模式，扩展了ICL中上下文概念的传统概念。HiAR-ICL引入了五种原子推理动作作为构建链式模式的基本组成部分。利用蒙特卡洛树搜索，我们探索推理路径并构建思维卡，以指导后续推理。然后，我们开发了一个动态匹配问题与适当思维卡的认知复杂性框架。实验结果表明HiAR-ICL的有效性，在MATH基准测试中使用Qwen2.5-7B-Instruct取得了最先进的准确率（79.6%），超过了GPT-4o（76.6%）和Claude 3.5（71.1%）。

English

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%).