Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback
This work addresses the challenge of enabling conversational AI to adapt and learn new skills post-deployment, which is incremental as it builds on existing feedback methods.
The paper tackles the problem of improving open-domain dialogue systems after deployment by using internet retrieval and human feedback, finding that the Director model significantly outperforms other approaches.
Frozen models trained to mimic static datasets can never improve their performance. Models that can employ internet-retrieval for up-to-date information and obtain feedback from humans during deployment provide the promise of both adapting to new information, and improving their performance. In this work we study how to improve internet-driven conversational skills in such a learning framework. We collect deployment data, which we make publicly available, of human interactions, and collect various types of human feedback -- including binary quality measurements, free-form text feedback, and fine-grained reasons for failure. We then study various algorithms for improving from such feedback, including standard supervised learning, rejection sampling, model-guiding and reward-based learning, in order to make recommendations on which type of feedback and algorithms work best. We find the recently introduced Director model (Arora et al., '22) shows significant improvements over other existing approaches.