A white-box hallucination detector approach treats the Large Language Model as a dynamic graph and analyzes structural properties of internal attention mechanisms. This method extracts spectral features, specifically eigenvalues, from attention maps to predict fabrication: factual retrieval produces stable eigen-structures, while hallucination leads to diffuse, chaotic patterns. This detector operates independently of generated semantic content and was evaluated across seven QA benchmarks (NQ-Open, TriviaQA, CoQA, SQuADv2, HaluEval-QA, TruthfulQA, GSM8K) using AUROC, Precision, Recall, and Cohen's Kappa metrics.

Authors

• Person: Not available Organization: GitHub
  EdinburghNLP/awesome-hallucination-detection - GitHub

Sources

• EdinburghNLP/awesome-hallucination-detection - GitHub github.com GitHub via serper

Referenced by nodes (9)

• TruthfulQA concept
• Precision concept
• recall concept
• TriviaQA concept
• AUROC concept
• SQuAD concept
• HaluEval concept
• NQ-Open concept
• attention mechanism concept