迈向具有偏见限制的LLM评判系统的可证明无偏评判
Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation
AI总结：
本文提出平均偏见限制框架，通过可验证的偏见限制保证，提升LLM评判系统的公平性与可靠性。
摘要：
随着AI模型从简单的聊天机器人发展到更复杂的流程，我们逐渐接近一个临界点，超过这个临界点，AI系统将被用于自主、自我维护的反馈循环中。任何自主的AI系统都依赖于自动化、可验证的奖励和反馈；在地面真实稀少或非确定性的情况下，一个实用的奖励来源是LLM作为评判者。尽管LLM评判者持续改进，但文献尚未引入能够强制执行标准并提供强保证的系统，特别是在偏见向量未知或被敌对发现的情况下。为了解决这个问题，我们提出了平均偏见限制（A-BB），一种算法框架，它正式保证了由于LLM评判者中的任何可测量偏见而减少伤害/影响。在Arena-Hard-Auto上评估四个LLM评判者，我们实现了（tau=0.5，delta=0.01）的偏见限制保证，同时在格式和图表偏见设置中保留了61-99%的原始排名相关性，大多数评判者偏见组合超过80%。我们的发现可复现的代码可在https://github.com/penfever/bias-bounded-evaluation上找到。
英文摘要：
As AI models progress beyond simple chatbots into more complex workflows, we draw ever closer to the event horizon beyond which AI systems will be utilized in autonomous, self-maintaining feedback loops. Any autonomous AI system will depend on automated, verifiable rewards and feedback; in settings where ground truth is sparse or non-deterministic, one practical source of such rewards is an LLM-as-a-Judge. Although LLM judges continue to improve, the literature has yet to introduce systems capable of enforcing standards with strong guarantees, particularly when bias vectors are unknown or adversarially discovered. To remedy this issue, we propose average bias-boundedness (A-BB), an algorithmic framework which formally guarantees reductions of harm/impact as a result of any measurable bias in an LLM judge. Evaluating on Arena-Hard-Auto with four LLM judges, we achieve (tau=0.5, delta=0.01) bias-bounded guarantees while retaining 61-99% correlation with original rankings across formatting and schematic bias settings, with most judge-bias combinations exceeding 80%. The code to reproduce our findings is available at https://github.com/penfever/bias-bounded-evaluation.
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。
