RLVF: Learning from Verbal Feedback without Overgeneralization
Authors: name: Moritz Stephan, name: Alexander Khazatsky, name: Eric Mitchell, name: Annie S Chen, name: Sheryl Hsu, name: Archit Sharma, name: Chelsea Finn

The diversity of contexts in which large language models (LLMs) are deployed
requires the ability to modify or customize default model behaviors to
incorporate nuanced requirements and preferences. A convenient interface to
specify such model adjustments is high-level verbal feedback, such as "Don't
use emojis when drafting emails to my boss." However, while writing high-level
feedback is far simpler than collecting annotations for reinforcement learning
from human feedback (RLHF), we find that simply prompting a model with such
feedback leads to overgeneralization of the feedback to contexts where it is
not relevant. We study the problem of incorporating verbal feedback without
such overgeneralization, inspiring a new method Contextualized Critiques with
Constrained Preference Optimization (C3PO). C3PO uses a piece of high-level
feedback to generate a small synthetic preference dataset specifying how the
feedback should (and should not) be applied. It then fine-tunes the model in
accordance with the synthetic preference data while minimizing the divergence
from the original model for prompts where the feedback does not apply. Our
experimental results indicate that our approach effectively applies verbal
feedback to relevant scenarios while preserving existing behaviors for other
contexts. For both human- and GPT-4-generated high-level feedback, C3PO
effectively adheres to the given feedback comparably to in-context baselines
while reducing overgeneralization by 30%.