评估大型语言模型自动判断系统的可靠性和准确性
Evaluating the Reliability and Fidelity of Automated Judgment Systems of Large Language Models
AI总结：
本文评估了大型语言模型作为自动质量评估器的适用性，通过测试37种不同规模的对话型LLM与5种不同判断提示的组合，展示了其在特定任务中与人类评估的高相关性。
摘要：
一个大型语言模型（LLM）作为判断者评估受害机器学习（ML）模型，特别是LLM，通过分析其输出。一个LLM作为判断者是结合了一个模型和一个专门设计的判断提示，该提示包含分析的准则。由此产生的分析自动化使对受害模型自由文本输出的复杂评估得以扩展，相较于人工评审，判断更快速且一致。因此，LLM的质量和安全评估可以涵盖受害模型的广泛使用场景。作为一种相对新的技术，LLM作为判断者缺乏对其可靠性和与人类判断一致性的深入研究。我们的工作评估了LLM作为受害LLM自动质量评估器的适用性。我们测试了37种不同规模的对话型LLM与5种不同判断提示、第二级判断概念以及5种针对任务进行微调的模型作为评估者。作为评估目标，我们为八种不同的判断任务类别和相应的地面真实标签基于人类评估进行了数据集的整理。我们的实证结果表明，当结合合适的提示时，LLM作为判断者与人类评估之间存在高度相关性，特别是对于GPT-4o、几个具有≥32B参数的开源模型以及少数较小的模型如Qwen2.5 14B。
英文摘要：
A Large Language Model (LLM) as judge evaluates the quality of victim Machine Learning (ML) models, specifically LLMs, by analyzing their outputs. An LLM as judge is the combination of one model and one specifically engineered judge prompt that contains the criteria for the analysis. The resulting automation of the analysis scales up the complex evaluation of the victim models' free-form text outputs by faster and more consistent judgments compared to human reviewers. Thus, quality and security assessments of LLMs can cover a wide range of the victim models' use cases. Being a comparably new technique, LLMs as judges lack a thorough investigation for their reliability and agreement to human judgment. Our work evaluates the applicability of LLMs as automated quality assessors of victim LLMs. We test the efficacy of 37 differently sized conversational LLMs in combination with 5 different judge prompts, the concept of a second-level judge, and 5 models fine-tuned for the task as assessors. As assessment objective, we curate datasets for eight different categories of judgment tasks and the corresponding ground-truth labels based on human assessments. Our empirical results show a high correlation of LLMs as judges with human assessments, when combined with a suitable prompt, in particular for GPT-4o, several open-source models with $\geqslant$ 32B parameters, and a few smaller models like Qwen2.5 14B.
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。
