Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph

ACL ARR 2026 January Submission930 Authors

Keywords: Retrieval-Augmented Language Model, Hallucination Detection, Model Attribution

Abstract:

The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress.
It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist.
Previous methods for detecting faithfulness hallucinations either neglect to capture the models' internal reasoning processes or handle those features coarsely, making it difficult for discriminators to learn.
This paper proposes a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination.
Specifically, we first extend the layer-wise relevance propagation algorithm from the token level to the semantic level, constructing an internal reasoning graph based on attribution vectors.
This provides a more faithful semantic-level representation of dependency.
Furthermore, we design a general framework based on a small pre-trained language model to utilize the dependencies in LLM’s reasoning for training and hallucination detection, which can dynamically adjust the pass rate of correct samples through a threshold.
Experimental results demonstrate that our method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k. Implementation available here: https://anonymous.4open.science/r/SIRG-1022.

Paper Type: Long

Research Area: Retrieval-Augmented Language Models

Research Area Keywords: retrieval-augmented generation, feature attribution, reasoning

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 930