Humans follow criteria when they execute tasks, and these criteria are
directly used to assess the quality of task completion. Therefore, having
models learn to use criteria to provide feedback can help humans or models to
perform tasks better. However, existing research in this field tends to
consider only a limited set of criteria or quality assessment aspects. To fill
this gap, we propose a general framework that enables large language models
(LLMs) to use comprehensive criteria for a task in delivering natural language
feedback on task execution. In particular, we present a model-in-the-loop
framework that semi-automatically derives criteria from collected guidelines
for different writing tasks and constructs in-context demonstrations for each
criterion. We choose three tasks from real-world scenarios to operationalize
this idea: paper introduction writing, Python code writing, and Reddit post
writing, and evaluate our feedback generation framework using different LLMs.
The results reveal the fine-grained effects of incorporating criteria and
demonstrations and provide valuable insights on how to teach LLMs to use
criteria more effectively.

通过使用全面的准则为任务提供自然语言反馈，我们提出了一个允许大型语言模型使用准则的通用框架，然后在真实场景中的三个任务中评估了我们的反馈生成框架，揭示了整合准则和示范的细致效果，并提供了有关如何教授大型语言模型更有效使用准则的宝贵见解。