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Uncovering Deceptive Tendencies in Language Models: A Simulated Company AI Assistant

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arxiv 2405.01576 v1 pith:RJQ7OPHV submitted 2024-04-25 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords companyassistantbehavedeceptivelymodelmodelspressurerealistic
verification ladder T0 review T1 audit T2 compute T3 formal
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We study the tendency of AI systems to deceive by constructing a realistic simulation setting of a company AI assistant. The simulated company employees provide tasks for the assistant to complete, these tasks spanning writing assistance, information retrieval and programming. We then introduce situations where the model might be inclined to behave deceptively, while taking care to not instruct or otherwise pressure the model to do so. Across different scenarios, we find that Claude 3 Opus 1) complies with a task of mass-generating comments to influence public perception of the company, later deceiving humans about it having done so, 2) lies to auditors when asked questions, and 3) strategically pretends to be less capable than it is during capability evaluations. Our work demonstrates that even models trained to be helpful, harmless and honest sometimes behave deceptively in realistic scenarios, without notable external pressure to do so.

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Cited by 2 Pith papers

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  1. Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language

    cs.AI 2025-07 conditional novelty 5.0 of 10

    Research on AI 'scheming' repeats the methodological errors of 1970s ape language studies, relying on anecdote and mentalistic interpretation instead of controlled, theory-driven tests.

  2. A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks

    cs.CL 2025-02 conditional novelty 3.0 of 10

    A narrative review of behavioral and representational Theory of Mind in LLMs, with a taxonomy of safety risks and mitigation directions.

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