Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement

Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Clark

Abstract:

Our goal is a teachable reasoning system for
question-answering (QA), where a user can interact
with faithful answer explanations, and
correct its errors so that the system improves
over time. Our approach is to augment a QA
model with a dynamic memory of user feedback,
containing user-supplied corrections to
erroneous model beliefs that users identify during
interaction. Retrievals from memory are
used as additional context for QA, to help avoid
previous mistakes in similar new situations -
a novel application of memory-based continuous
learning. With simulated feedback, we
find that our system (called TeachMe1) continually
improves with time, and without model
retraining, requiring feedback on only 25% of
training examples to reach within 1% of the
upper-bound (feedback on all examples). Similarly,
in experiments with real users, we observe
a similar trend, with performance improving
by over 15% on a hidden test set after teaching.
This suggests new opportunities for using
frozen language models in an interactive setting
where users can inspect, debug, and correct the
model’s beliefs, leading to improved system’s
performance over time.