Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness
Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether large language models possess similar privileged knowledge about answer correctness, information unavailable through external observation. We train correctness classifiers on question representations from both a model's own hidden states and external models, testing whether self-representations provide a performance advantage. On standard evaluation, we find no advantage: self-probes perform comparably to peer-model probes. We hypothesize this is due to high inter-model agreement of answer correctness. To isolate genuine privileged knowledge, we evaluate on disagreement subsets, where models produce conflicting predictions. Here, we discover domain-specific privileged knowledge: self-representations consistently outperform peer representations in factual knowledge tasks, but show no advantage in math reasoning. We further localize this domain asymmetry across model layers, finding that the factual advantage emerges progressively from early-to-mid layers onward, consistent with model-specific memory retrieval, while math reasoning shows no consistent advantage at any depth.
69df28f1d3c84fcbf2921905
Tomer Ashuach
69df28f1d3c84fcbf2921906
Liat Ein-Dor
69df28f1d3c84fcbf2921907
Shai Gretz
69df28f1d3c84fcbf2921908
Yoav Katz
69df28f1d3c84fcbf2921909
Yonatan Belinkov
Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness
Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether large language models possess similar privileged knowledge about answer correctness, information unavailable through external observation. We train correctness classifiers on question representations from both a model's own hidden states and external models, testing whether self-representations provide a performance advantage. On standard evaluation, we find no advantage: self-probes perform comparably to peer-model probes. We hypothesize this is due to high inter-model agreement of answer correctness. To isolate genuine privileged knowledge, we evaluate on disagreement subsets, where models produce conflicting predictions. Here, we discover domain-specific privileged knowledge: self-representations consistently outperform peer representations in factual knowledge tasks, but show no advantage in math reasoning. We further localize this domain asymmetry across model layers, finding that the factual advantage emerges progressively from early-to-mid layers onward, consistent with model-specific memory retrieval, while math reasoning shows no consistent advantage at any depth.
Large language models do not demonstrate superior self-awareness of answer correctness compared to external models, though they show domain-specific advantages in factual knowledge tasks when models disagree on predictions.
large language models correctness classifiers hidden states question representations self-representations peer-model probes factual knowledge tasks math reasoning model layers inter-model agreement