LURE: 减少评估感知的实时使用回放评估
LURE: Live-Usage Replay Evaluations for Reducing Evaluation Awareness
AI总结：
提出LURE方法，通过回放真实代理交互轨迹并附加评估提示来构建类似部署的评估，以减少大语言模型的评估感知，并引入自动化评估真实性流程。
摘要：
大型语言模型能够识别自己正在被评估（评估感知），并因此表现出不同的行为，这破坏了安全和对齐基准的有效性。我们提出LURE（实时使用回放评估），一种通过回放真实的代理交互轨迹并在末尾附加评估提示来构建类似部署的评估的方法。我们还引入了一个自动化流程来衡量评估的真实性，结合了对口头化评估感知的检测和法官模型对日志是否为评估的概率估计，并在一个包含部署和评估记录的大型数据集上进行了验证。我们发现，与广泛使用的基准和合成评估生成器相比，基于LURE的评估与部署的区分度显著降低，并且可以接近与用户真实对话的真实性。我们在策划、AI安全破坏和谄媚场景中实例化了LURE。我们的结果表明，评估真实性是对齐基准的一个关键属性，应在基准结果旁边报告，特别是当这些结果用于安全案例时。
英文摘要：
Large language models can recognize when they are being evaluated (evaluation awareness) and behave differently because of that, which undermines the validity of safety and alignment benchmarks. We propose LURE (Live-Usage Replay Evaluations), a method for constructing deployment-like evaluations by replaying realistic agentic interaction trajectories and appending evaluation prompt at the end. We also introduce an automated pipeline for measuring evaluation realism, combining detection of verbalized evaluation awareness and judge-model estimates of the probability of logs being an evaluation, and validate it on a large dataset of deployment and evaluation transcripts. We find that LURE-based evaluations are substantially less distinguishable from deployment than widely used benchmarks and synthetic evaluation generators, and can approach the realism of real conversations with users. We instantiate LURE in scheming, AI safety sabotage, and sycophancy settings. Our results suggest that evaluation realism is a crucial property of alignment benchmarks and should be reported alongside benchmark results, especially when such results are used in safety cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
