A Lie Detector Test for Language Models: Reading Knowledge a Model Won't Reveal
PIR reads internal model states to detect concealed knowledge, distinguishing sandbagging from genuine ignorance across Gemma, Qwen, Llama, Mistral and Phi models.
Probe of Internal Recognition (PIR) adapts the forensic Concealed Information Test to LLM internals, reading which candidate answer a model recognizes as correct without any honest reference model or labeled truth corpus. Across eight models from five families it recovers concealed answers at 0.70-0.87 balanced accuracy versus a 0.25 chance rate, and remains robust (0.85-0.93) against prompted deception, trained sandbagging, password-locked, and circuit-broken checkpoints. Recognition drops to unknown-question levels only when unlearning actually removes knowledge, enabling sandbagging audits and unlearning verification.
- Reference-free probing separates models that will not answer from those that cannot
- 0.70-0.87 balanced accuracy recovering concealed answers across eight models
- Robust against sandbagging, prompted deception, password locks, and circuit breaking
- Enables sandbagging audits and verification of unlearning claims
From the original article
1 min read at huggingface.co
Large language models can hold knowledge they do not report. A model may sandbag on a capability evaluation, or answer against what it internally knows, and its outputs alone cannot tell whether it is hiding an answer or simply does not have one.
By Hiskias Dingeto · huggingface.co
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