AI Research
Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs
Training probes to detect deceptive outputs from large language models is still an open problem. Recent work has demonstrated that detection probes fail especially in out-of-domain scenarios -- training on one type of lie does not transfer well to deception scenarios involving ot...
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ArXiv CS.AI
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ArXiv CS.AI
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Understanding the nuances of deception in LLMs is crucial for building trustworthy AI. This research reveals that the type and structure of lies significantly impact our ability to detect them, emphasizing the need for diverse training data and context-aware detection methods.
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