Work on instruction-tuned Large Language Models (LLMs) has used automatic methods based on text overlap and LLM judgments as cost-effective alternatives to human evaluation. In this paper, we study the reliability of such methods across a broad range of tasks and in a cross-lingual setting. In contrast to previous findings, we observe considerable variability in correlations between automatic methods and human evaluators when scores are differentiated by task type. Specifically, the widely-used ROUGE-L metric strongly correlates with human judgments for short-answer English tasks but is unreliable in free-form generation tasks and cross-lingual transfer. The effectiveness of GPT-4 as an evaluator depends on including reference answers when prompting for assessments, which can lead to overly strict evaluations in free-form generation tasks. In summary, we find that, while automatic evaluation methods can approximate human judgements under specific conditions, their reliability is highly context-dependent. Our findings enhance the understanding of how automatic methods should be applied and interpreted when developing and evaluating instruction-tuned LLMs.

我们对基于文本重叠和大型语言模型判断的自动化方法在广泛任务和跨语言环境中的可靠性进行了研究，发现自动评估方法与人类评估者之间的相关性在任务类型不同的情况下存在相当大的变异性。尽管自动评估方法在特定条件下可以近似人类判断，但其可靠性高度依赖于上下文。这些发现强化了我们在开发和评估面向指导的大型语言模型时如何应用和解释自动化方法的理解。

自动评估方法在面向指导型语言模型中的可靠性研究