Explainable Hallucination Detection in Large Language Models Using Evidence-Grounded Verification

Abstract

This paper presents Evidence-Grounded Verification (EGV), a modular framework for explainable hallucination detection in large language models. EGV decomposes model outputs into atomic claims, retrieves independent evidence, performs fine-grained four-way verification (Supported, Partially Supported, Contradicted, Unverifiable), and generates template-constrained explanations that are causally linked to the underlying evidence. Unlike most existing detectors that output only a scalar score or binary label, EGV treats explainability as a core architectural requirement. The framework is designed to run on consumer hardware using freely available models. A pilot study on 300 items (600 claims) from the HaluEval QA benchmark (using oracle evidence) compares three verification backends. Results show that a hybrid decision rule combining an off-the-shelf NLI model with simple lexical and length-based features substantially outperforms pure NLI (F1 rises from 3.5% to 65.6%), while a counterfactual evidence-swap test achieves a faithfulness score of 78.0%. The paper provides a full evaluation protocol, an honest analysis of limitations, and clear directions for future validation. This is a Methodology / Framework paper intended as a practical starting point for research on transparent, evidence-grounded hallucination detection.