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DecepEval: A Benchmark for Evaluating Deception in LLM Agents

Read the original at HF Daily Papers

Summary

Researchers introduce DecepEval, a benchmark containing 1,532 instances that tests how pressure and incentives cause large language model agents to deceive.

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