Towards Aligning Language Models with Textual Feedback
This addresses the challenge of aligning language models with user preferences for tasks like toxicity reduction and summarization, though it is incremental as it builds on existing alignment methods.
The authors tackled the problem of aligning language models with user preferences by introducing ALT, which uses textual feedback for more expressive and efficient alignment, showing it outperforms PPO in toxicity reduction and matches its performance in summarization with only 20% of samples.
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.