NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models
2025-01-22 · ICLR 2025 Poster · anchor
Findings
- IC-592The log-likelihood layer in LLaMA-2-7B, LLaMA-2-7B-Chat, Vicuna-7B, and Mistral-7B-Instruct produces factually incorrect answers on TruthfulQA MC1 (817 samples) due to a misalignment between the output distribution and internal attention head representations, with LM-to-head-norm accuracy gaps of 24.23 to 40.68 points.
- IC-593The L2 norms of attention heads in Mistral-7B-Instruct and LLaMA-2-7B correlate with truthfulness, spiking by up to 83% at token positions of factual proposition completions and pertinent factual associations, and this correlation is specific to multi-headed attention representations rather than query, key, value, output, or FFN norms.
- IC-594In LLaMA-2-7B, the truth-correlated attention heads are concentrated after layer 9, with two functional types (structural and associative) evenly distributed throughout the upper portions of the model, showing no further depth-dependent specialisation within that region.