RESEARCH · RESEARCH · #725
Detecting hallucination in LLMs by tracing attention-graph topology (arXiv:2609.21096v1)
This arXiv preprint proposes a single-pass method for detecting hallucinated outputs from LLMs by analysing information-flow topology in attention graphs, using Forman-Ricci curvature to identify bottlenecks. The paper reports consistent improvements over prior attention-based and multi-response baselines on two hallucination-detection benchmarks and finds that hallucinations correlate with impaired context sharing (e.g., over-reliance on self-attention, diffused retrieval from earlier tokens, or information over-squashing), especially in the final transformer layer, across several LLMs.
KEY POINTS
- This arXiv preprint proposes a single-pass method for detecting hallucinated outputs from LLMs by analysing information-flow topology in attention graphs, using Forman-Ricci curvature to identify bottlenecks.
- The paper reports consistent improvements over prior attention-based and multi-response baselines on two hallucination-detection benchmarks and finds that hallucinations correlate with impaired context sharing (e.g., over-reliance on self-attention, diffused retrieval from earlier tokens, or information over-squashing), especially in the final transformer layer, across several LLMs.
- If robust, this topology-based single-pass signal could provide a lightweight, model-agnostic indicator of hallucination risk and guide debugging or mitigation in deployed LLMs.
WHY IT MATTERS
If robust, this topology-based single-pass signal could provide a lightweight, model-agnostic indicator of hallucination risk and guide debugging or mitigation in deployed LLMs.