Detecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
This detects hallucinations in a single forward pass by analyzing attention-graph topology (Forman-Ricci curvature) rather than sampling multiple responses, beating both attention-based and multi-response baselines across models and benchmarks. That means you can potentially flag unreliable outputs without the latency and token cost of self-consistency sampling, and the signal is concrete: hallucinations correlate with over-reliance on self-attention and information over-squashing in the final layer. Practically, it points toward a cheap, real-time guardrail you can wire into inference instead of a slow ensemble check.
Hallucinated responses in LLMs are consistently linked to impaired context sharing, characterized by over-reliance on self-attention, diffused context retrieval, or information over-squashing in the final transformer layer. This insight enables more precise detection of hallucinations in production, reducing reliance on multi-response methods and improving reliability of single-pass LLM outputs.