Continually Improving Extractive QA via Human Feedback
Published in The 2023 Conference on Empirical Methods in Natural Language Processing, 2023
This paper presents a framework for continually improving extractive question answering systems through human feedback. We demonstrate how models can learn from corrective feedback to improve their performance over time.
Recommended citation: Ge Gao*, Hung-Ting Chen*, Yoav Artzi, Eunsol Choi. (2023). "Continually Improving Extractive QA via Human Feedback." The 2023 Conference on Empirical Methods in Natural Language Processing.
Download Paper