在LLM评估之海中航行：探究毒性基准中的偏见
Navigating the Sea of LLM Evaluation: Investigating Bias in Toxicity Benchmarks
AI总结：
本文研究了毒性基准的鲁棒性，发现评估设置变化会导致基准行为差异，任务类型改变会增加有害内容被标记的倾向，且输入数据域变化时基准表现不稳定，需更完善的评估框架。
摘要：
LLM的快速应用凸显了安全部署的挑战，揭示了毒性基准系统评估的不足。随着组织依赖这些基准认证面向客户的应用和自动审核，未识别的评估偏见可能导致部署脆弱或不安全的系统。本文研究了现有基准设置的鲁棒性，考察了当前被忽视的内在偏见，如模型选择、指标和任务类型。实验发现，当评估设置改变时，基准行为存在显著差异。具体而言，将任务从文本生成改为摘要会增加基准将内容标记为有害的倾向。此外，某些基准在输入数据域变化时无法保持一致行为。我们还观察到模型特定的不稳定性，表明需要更稳健和全面的安全评估框架。
英文摘要：
The rapid adoption of LLMs in both research and industry highlights the challenges of deploying them safely and reveals a gap in the systematic evaluation of toxicity benchmarks. As organizations increasingly rely on these benchmarks to certify models for customer-facing applications and automated moderation, unrecognized evaluation biases could lead to the deployment of vulnerable or unsafe systems. This work investigates the robustness of established benchmarking setups and examines how to measure currently neglected intrinsic biases, such as those related to model choice, metrics, and task types. Our experiments uncover significant discrepancies in benchmark behaviors when evaluation setups are altered. Specifically, shifting the task from text completion to summarization increases the tendency of benchmarks to flag content as harmful. Additionally, certain benchmarks fail to maintain consistent behavior when the input data domain is changed. Furthermore, we observe model-specific instabilities, demonstrating a clear need for more robust and comprehensive safety evaluation frameworks.
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。
