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Keyword: deception detection ×Clear all

1 claim from 1 paper

1. Computer Science › Adversarial Robustness in Machine Learning

   Detecting Strategic Deception Using Linear Probes

   Goldowsky-Dill, Chughtai, Heimersheim and Hobbhahn · arXiv (Cornell University) · 2025

   The authors test whether linear probes on a language model's internal activations can detect deceptive responses, and conclude they look promising but are not yet a robust defence.

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   1. Supported · 71%In this paper's tests, a linear probe on Llama-3.3-70B-Instruct separated honest from deceptive responses with AUROCs of 0.96 to 0.999.“We find that our probe distinguishes honest and deceptive responses with AUROCs between 0.96 and 0.999 on our evaluation datasets. If we set the decision threshold to have a 1% false positive rate on chat data not related to deception, our probe catches 95-99% of the deceptive responses.”