DecepEval: A Benchmark for Evaluating Deception in LLM Agents
As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment. Existing evaluations show that LLM agents can deceive, but often examine isolated scenarios or narrowly defined conditions, limiting systematic understanding of when deception becomes more likely. To address t
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As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment. Existing evaluations show that LLM agents can deceive, but often examine isolated scenarios or narrowly defined conditions, limiting systematic understanding of when deception becomes more likely. To address t
As LLM agents become more autonomous, understanding and mitigating deceptive behavior is crucial for their safe and ethical deployment. This benchmark provides a systematic way to evaluate deception, which is vital for building trustworthy AI systems and ensuring regulatory compliance.