Related papers: Continually Improving Extractive QA via Human Feedback

Continually Improving Extractive QA via Human Feedback

URL: http://arxiv.org/abs/2305.12473v2
Date: Fri, 3 Nov 2023 18:23:58 GMT
Title: Continually Improving Extractive QA via Human Feedback
Authors: Ge Gao, Hung-Ting Chen, Yoav Artzi and Eunsol Choi
Abstract summary: We study continually improving an extractive question answering (QA) system via human user feedback. We conduct experiments involving thousands of user interactions under diverse setups to broaden the understanding of learning from feedback over time.
Score: 59.49549491725224
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: We study continually improving an extractive question answering (QA) system via human user feedback. We design and deploy an iterative approach, where information-seeking users ask questions, receive model-predicted answers, and provide feedback. We conduct experiments involving thousands of user interactions under diverse setups to broaden the understanding of learning from feedback over time. Our experiments show effective improvement from user feedback of extractive QA models over time across different data regimes, including significant potential for domain adaptation.

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