评估你无法评估的内容：生成响应的不可评估质量问题
Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
AI总结：
本文构建了两个对抗性元评估数据集KdConv-ADV和DSTC7-ADV，揭示基于LLMs的无参考评估器在封闭式对话响应中识别不合理内容的能力不足，存在可靠性风险。
摘要：
诸如ChatGPT等大语言模型（LLMs）已展现出卓越的语言理解与生成能力。尽管基于LLMs的无参考评估器比传统的基于参考的评估器表现出更好的人类一致性，但使用基于LLMs的无参考评估器仍面临诸多挑战。无参考评估器更适用于具有不同语义响应的开放式示例。但并非所有示例都是开放式的。对于具有唯一正确语义响应的封闭式示例，当给出与事实和参考语义不一致的响应时，无参考评估器仍会认为其质量较高。为了全面评估基于LLMs的评估器的可靠性，我们分别基于KdConv和DSTC7-AVSD构建了两个对抗性元评估对话生成数据集KdConv-ADV和DSTC7-ADV。与以往的元评估基准相比，KdConv-ADV和DSTC7-ADV更具挑战性，因为它们要求评估器能够借助外部知识甚至自身知识合理地评估封闭式示例。实证结果表明，LLMs识别不合理响应的能力不足。使用基于LLMs的无参考评估器来评估对话响应质量存在风险。
英文摘要：
LLMs (large language models) such as ChatGPT have shown remarkable language understanding and generation capabilities. Although reference-free evaluators based on LLMs show better human alignment than traditional reference-based evaluators, there are many challenges in using reference-free evaluators based on LLMs. Reference-free evaluators are more suitable for open-ended examples with different semantics responses. But not all examples are open-ended. For closed-ended examples with unique correct semantic response, reference-free evaluators will still consider it high quality when giving a response that is inconsistent with the facts and the semantic of reference. In order to comprehensively evaluate the reliability of evaluators based on LLMs, we construct two adversarial meta-evaluation dialogue generation datasets KdConv-ADV and DSTC7-ADV based on KdConv and DSTC7-AVSD, respectively. Compared to previous meta-evaluation benchmarks, KdConv-ADV and DSTC7-ADV are much more challenging since they requires evaluators to be able to reasonably evaluate closed-ended examples with the help of external knowledge or even its own knowledge. Empirical results show that the ability of LLMs to identify unreasonable responses is insufficient. There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
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。
