模仿人类解题结构，用分组检索提升复杂问题推理能力
GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem Structuring
- 基于知识关键点分组，从多个概念起点同时检索与推理
- 在医学和法律考试数据集上显著优于传统RAG与思维链方法
- 适合需要多角度分析的复杂推理任务，如医疗诊断、法律判案
语言模型的表现常受限于知识不足和推理能力有限。以往方法如检索增强生成（RAG）和思维链（CoT）通过引入外部知识或强制线性推理链来改善，但在真实场景中表现下降。受认知科学启发，人类解题是搜索结构化问题空间而非单一推理链。我们提出GroupRAG，一种基于知识驱动关键点分组的、认知启发的群体感知检索与推理框架。该方法识别问题中的潜在结构分组，从多个概念起点进行检索与推理，实现两过程的细粒度交互。在MedQA（医学）和Bar Exam QA（法律）数据集上的实验表明，GroupRAG优于代表性RAG与CoT基线。结果表明，借鉴人类认知显式建模问题结构，是提升鲁棒检索增强推理的有前景方向。
原文摘要 · Abstract (English)
The performance of language models is commonly limited by insufficient knowledge and constrained reasoning. Prior approaches such as Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) address these issues by incorporating external knowledge or enforcing linear reasoning chains, but often degrade in real-world settings. Inspired by cognitive science, which characterizes human problem solving as search over structured problem spaces rather than single inference chains, we argue that inadequate awareness of problem structure is a key overlooked limitation. We propose GroupRAG, a cognitively inspired, group-aware retrieval and reasoning framework based on knowledge-driven keypoint grouping. GroupRAG identifies latent structural groups within a problem and performs retrieval and reasoning from multiple conceptual starting points, enabling fine-grained interaction between the two processes. Experiments on MedQA (medical) and Bar Exam QA (legal) show that GroupRAG outperforms representative RAG- and CoT-based baselines. These results suggest that explicitly modeling problem structure, as inspired by human cognition, is a promising direction for robust retrieval-augmented reasoning.
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