Researchers propose topology-based method to detect LLM hallucinations
A new arXiv paper investigates how the shape of attention‑graph information flow can reveal hallucinated outputs from large language models. The authors compute Forman‑Ricci curvature on attention graphs to expose structural bottlenecks such as over‑reliance on self‑attention, diffused context retrieval, or information over‑squashing, especially in the final transformer layer.
Key points
- Analyzes Forman‑Ricci curvature of attention graphs to identify information bottlenecks linked to hallucinations
- Captures semi‑local and global attention‑head flow traits in a single‑pass detection method
- Shows consistent gains over prior baselines on two hallucination‑detection benchmarks across multiple LLMs
The study introduces a single‑pass technique that captures both semi‑local and global flow characteristics of attention heads associated with hallucinations. Experiments across several unnamed LLMs and two established hallucination‑detection benchmarks show the method consistently outperforms existing attention‑based and multi‑response baselines, while remaining competitive across diverse model architectures. The authors argue that impaired context sharing during causal generation is a key driver of hallucination, and their topological signatures provide a practical diagnostic tool for future model development and safety evaluation.
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