Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises

• URL: http://arxiv.org/abs/2511.04020v1
• Date: Thu, 06 Nov 2025 03:37:24 GMT
• Title: Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises
• Authors: Shiyin Lin,
• Abstract summary: We propose a framework that integrates abductive inference into retrieval-augmented LLMs.<n> Experimental results on abductive reasoning and multi-hop QA benchmarks show that our approach improves both answer accuracy and reasoning faithfulness.<n>This work highlights abductive inference as a promising direction for enhancing the robustness and explainability of RAG systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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