Neural Message-Passing on Attention Graphs for Hallucination Detection
This addresses the issue of unreliable content generation in LLMs for users and developers, representing an incremental advance by combining existing computational traces into a novel graph-based method.
The paper tackles the problem of detecting hallucinations in Large Language Models by unifying attention and activation signals into attributed graphs and applying Graph Neural Networks, achieving consistent performance improvements across diverse benchmarks and showing promising zero-shot transfer capabilities.
Large Language Models (LLMs) often generate incorrect or unsupported content, known as hallucinations. Existing detection methods rely on heuristics or simple models over isolated computational traces such as activations, or attention maps. We unify these signals by representing them as attributed graphs, where tokens are nodes, edges follow attentional flows, and both carry features from attention scores and activations. Our approach, CHARM, casts hallucination detection as a graph learning task and tackles it by applying GNNs over the above attributed graphs. We show that CHARM provably subsumes prior attention-based heuristics and, experimentally, it consistently outperforms other leading approaches across diverse benchmarks. Our results shed light on the relevant role played by the graph structure and on the benefits of combining computational traces, whilst showing CHARM exhibits promising zero-shot performance on cross-dataset transfer.