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The Pentagon is requesting $30 million to develop an AI-powered lie detector called Polygraph+ to improve accuracy and reliability, amid internal tensions and previous failed attempts.
This paper scales the SOLiD lie-detector oversight method to larger LLMs (up to 405B parameters) and evaluates it in realistic preference-learning settings, finding that undetected deception decreases with model scale but that the method is sensitive to distribution shift between training data.
This paper evaluates four lie detection methods for language models across prompted lying and trained model organisms, finding that activation- and logprob-based detectors drop sharply on trained model organisms while a chain-of-thought judge remains strong. It introduces new testbeds and the Did-You-Lie (DYL) follow-up probe method, releasing datasets and model organisms.