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Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
2024-01-16
· ICLR 2024 poster ·
anchor
Findings
IC-1261
LLaMA-2 attention to constraint tokens correlates with factual correctness, and a linear probe on these attention weights predicts factual errors comparably to model confidence
IC-1262
LLaMA-2 factual query accuracy improves with entity popularity and decreases with query constrainedness, with larger models showing better performance on less popular and more constrained queries
IC-1263
LLaMA-2 7B and 13B attention signal for predicting factual errors is available by approximately 50% of layers, enabling early stopping without performance degradation, while LLaMA-2 70B shows a slight performance drop