Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models (LLMs) to human preferences. Despite the diversity of the information it can convey, NLF methods are often hand-designed and arbitrary, with little systematic grounding. At the same time, research in learning sciences 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 various characteristics of the feedback space, and a feedback content taxonomy based on these variables, providing a general mapping of the feedback space. In addition to streamlining 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 research.

翻译：自然语言反馈（NLF）正日益成为对齐大型语言模型（LLM）与人类偏好的重要机制。尽管NLF能够传递多样化的信息，但其方法往往缺乏系统性基础，多为手工设计且带有随意性。与此同时，学习科学领域的研究早已确立了多种有效的反馈模型。在这篇观点性论文中，我们整合教育学理论，提出了FELT——一个面向LLM的反馈框架。该框架系统阐述了反馈空间的多维特征，并基于这些变量构建了反馈内容分类体系，从而为反馈空间提供了通用映射模型。FELT不仅能够优化NLF的设计流程，更为NLF研究揭示了全新的探索方向。我们向学界公开此分类体系，并提供分类指南与实例解析，以促进未来研究对本分类框架的运用。