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

arXiv:2511.04020v1 Announce Type: cross
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, emphabductive 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.