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.

翻译：诸如ChatGPT等大型语言模型（LLMs）展现了卓越的语言理解与生成能力。尽管基于LLM的无参考评估器相比传统有参考评估器更符合人类判断，但其应用仍面临诸多挑战。无参考评估器更适用于语义响应多样化的开放式示例。然而，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，无参考评估器仍可能在响应与事实及参考语义不一致时判定其为高质量。为全面评估基于LLM的评估器可靠性，我们基于KdConv和DSTC7-AVSD分别构建了两个对抗性元评估对话生成数据集KdConv-ADV与DSTC7-ADV。与以往的元评估基准相比，KdConv-ADV与DSTC7-ADV更具挑战性，因为它们要求评估器能借助外部知识甚至自身知识合理评估封闭式示例。实证结果表明，LLM识别不合理响应的能力不足，使用基于LLM的无参考评估器评估对话响应质量存在风险。