@inproceedings{ashuach-etal-2026-masked,
title = "Masked by Consensus: Disentangling Privileged Knowledge in {LLM} Correctness",
author = "Ashuach, Tomer and
Gretz, Shai and
Katz, Yoav and
Belinkov, Yonatan and
Ein-Dor, Liat",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.483/",
pages = "10577--10596",
ISBN = "979-8-89176-390-6",
abstract = "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."
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%0 Conference Proceedings
%T Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness
%A Ashuach, Tomer
%A Gretz, Shai
%A Katz, Yoav
%A Belinkov, Yonatan
%A Ein-Dor, Liat
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ashuach-etal-2026-masked
%X 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.
%U https://aclanthology.org/2026.acl-long.483/
%P 10577-10596
Markdown (Informal)
[Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness](https://aclanthology.org/2026.acl-long.483/) (Ashuach et al., ACL 2026)
ACL
- Tomer Ashuach, Shai Gretz, Yoav Katz, Yonatan Belinkov, and Liat Ein-Dor. 2026. Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10577–10596, San Diego, California, United States. Association for Computational Linguistics.