knowledge-recall

Researchers trace how LLMs recall relational facts by probing per-head attention contributions, showing these are strong linear features whose fidelity correlates with relation specificity and entity connectedness.

This paper challenges the assumption that LLMs can reliably distinguish between hallucinated and factual outputs through internal signals, arguing that internal states primarily reflect knowledge recall rather than truthfulness. The authors propose a taxonomy of hallucinations (associated vs. unassociated) and show that associated hallucinations exhibit hidden-state geometries overlapping with factual outputs, making standard detection methods ineffective.