让检索增强模型自动补全推理缺失前提，提升问答准确率与可信度。
Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises
- 检测证据不足后生成合理假设前提
- 通过一致性和合理性验证候选前提
- 适合需要严谨推理的问答系统开发者
检索增强生成（RAG）模型在知识密集型任务中表现优异，但当检索到的证据不完整时，推理过程会出现断点。本文提出一种将溯因推理（abductive inference）融入RAG框架的方法：自动识别证据不足情况，生成可能的缺失前提，并通过一致性与合理性检验进行验证。在溯因推理和多跳问答基准测试上，该方法显著提升了答案准确率与推理可信度。结果表明，溯因推理是增强RAG系统鲁棒性与可解释性的有效路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) enhanced with retrieval -- commonly referred to as Retrieval-Augmented Generation (RAG) -- have demonstrated strong performance in knowledge-intensive tasks. However, RAG pipelines often fail when retrieved evidence is incomplete, leaving gaps in the reasoning process. In such cases, \emph{abductive inference} -- the process of generating plausible missing premises to explain observations -- offers a principled approach to bridge these gaps. In this paper, we propose a framework that integrates abductive inference into retrieval-augmented LLMs. Our method detects insufficient evidence, generates candidate missing premises, and validates them through consistency and plausibility checks. Experimental results on abductive reasoning and multi-hop QA benchmarks show that our approach improves both answer accuracy and reasoning faithfulness. This work highlights abductive inference as a promising direction for enhancing the robustness and explainability of RAG systems.
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