Leveraging Implicit Feedback from Deployment Data in Dialogue
Authors: name: Richard Yuanzhe Pang, name: Stephen Roller, name: Kyunghyun Cho, name: He He, name: Jason Weston

We study improving social conversational agents by learning from natural
dialogue between users and a deployed model, without extra annotations. To
implicitly measure the quality of a machine-generated utterance, we leverage
signals like user response length, sentiment and reaction of the future human
utterances in the collected dialogue episodes. Our experiments use the publicly
released deployment data from BlenderBot (Xu et al., 2023). Human evaluation
indicates improvements in our new models over baseline responses; however, we
find that some proxy signals can lead to more generations with undesirable
properties as well. For example, optimizing for conversation length can lead to
more controversial or unfriendly generations compared to the baseline, whereas
optimizing for positive sentiment or reaction can decrease these behaviors.