Summary
A new reference-free method has been developed to detect hidden behaviors and biases in LLMs, eliminating the need for a base model comparison. This technique, which uses approximately 100 chat calls, effectively matches or surpasses existing auditing methods on benchmarks. Crucially, it can also uncover RLHF-induced opinion biases on socially sensitive topics in models like Llama 70B.
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