Natural Language Feedback (NLF) is an increasingly popular avenue to align Large Language Models (LLMs) to human preferences. Despite the richness and diversity of the information it can convey, NLF is often hand-designed and arbitrary. In a different world, research in pedagogy has long established several effective feedback models. In this opinion piece, we compile ideas from pedagogy to introduce FELT, a feedback framework for LLMs that outlines the various characteristics of the feedback space, and a feedback content taxonomy based on these variables. Our taxonomy offers both a general mapping of the feedback space, as well as pedagogy-established discrete categories, allowing us to empirically demonstrate the impact of different feedback types on revised generations. In addition to streamlining existing NLF designs, FELT also brings out new, unexplored directions for research in NLF. We make our taxonomy available to the community, providing guides and examples for mapping our categorizations to future resources.

翻译：自然语言反馈（NLF）是一种越来越流行的对齐大型语言模型（LLMs）与人类偏好的方法。尽管它能够传递丰富多样的信息，但NLF通常是手工设计的且具有任意性。在另一个领域中，教育学研究早已确立了多种有效的反馈模型。在本篇观点性文章中，我们从教育学中整合观点，引入FELT——一个针对LLMs的反馈框架，它勾勒出反馈空间的各种特征，并基于这些变量提出一个反馈内容分类体系。我们的分类体系既提供了反馈空间的一般性映射，又提供了基于教育学的离散类别，使我们能够通过实验证明不同反馈类型对修正后生成内容的影响。除了简化现有的NLF设计外，FELT还开辟了NLF研究中尚未探索的新方向。我们向社区公开我们的分类体系，提供指南和示例，用于将我们的分类映射到未来的资源中。