most citedSleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

39 citations · 113 across the 7 of their papers we have counts for

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cs.CL2024★ 2 cited

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

Olli Järviniemi, Evan Hubinger

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 assis…

cs.CL2023★ 8 cited

Question Decomposition Improves the Faithfulness of Model-Generated Reasoning

Ansh Radhakrishnan, Karina Nguyen, Anna Chen +21

As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior. One approach to help with this issue is to p…