We present ALT (ALignment with Textual feedback), an approach that aligns language models with user preferences expressed in text. We argue that text offers greater expressiveness, enabling users to provide richer feedback than simple comparative preferences and this richer feedback can lead to more efficient and effective alignment. ALT aligns the model by conditioning its generation on the textual feedback. Our method relies solely on language modeling techniques and requires minimal hyper-parameter tuning, though it still presents the main benefits of RL-based alignment algorithms and can effectively learn from textual feedback. We explore the efficacy and efficiency of textual feedback across different tasks such as toxicity reduction, summarization, and dialog response generation. We find that ALT outperforms PPO for the task of toxicity reduction while being able to match its performance on summarization with only 20% of the samples. We also explore how ALT can be used with feedback provided by an existing LLM where we explore an LLM providing constrained and unconstrained textual feedback. We also outline future directions to align models with natural language feedback.

本研究针对语言模型与用户文本反馈对齐的问题，提出了一种新方法ALT，该方法通过用户富有表现力的文本反馈来提高模型对齐的效率与效果。研究表明，ALT在减少毒性方面超越了PPO，并在摘要生成任务中能够以20%的样本量匹配PPO的性能，展示了与自然语言反馈对齐的巨大潜力。

朝着与文本反馈对齐的语言模型