Seamless Deception: Larger Language Models Are Better Knowledge Concealers
Language Models (LMs) may acquire harmful knowledge, and yet feign ignorance of these topics when under audit. Inspired by the recent discovery of deception-related behaviour patterns in LMs, we aim to train classifiers that detect when a LM is actively concealing knowledge. Initial findings on smaller models show that classifiers can detect concealment more reliably than human evaluators, with gradient-based concealment proving easier to identify than prompt-based methods. However, contrary to prior work, we find that the classifiers do not reliably generalize to unseen model architectures and topics of hidden knowledge. Most concerningly, the identifiable traces associated with concealm...
| Authors | Dhananjay Ashok, Ruth-Ann Armstrong, Jonathan May |
|---|---|
| Primary category | cs.CL |
| Published | 15 March 2026 |
| Last updated at source | 20 March 2026 |
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