Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment

By Zichao Li, Prakhar Sharma, Xing Han Lù, Jackie Chi Kit Cheung, Siva Reddy. 2022

32 citations recorded by the source corpus.

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment.In this paper, we ask the question: Can we improve QA systems further post-deployment based on user interactions? We focus on two kinds of improvements: 1) improving the QA system’s performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer.We collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers.The feedback contains both structured ratings and unstructured natural language explanations.We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems. The generated explanations also help users make informed decisions about the correctness of answers.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Training Language Models with Language Feedback (2022, background): “A few prior works also learn from language feedback, for the purpose of ranking candidate outputs rather than generating outputs (Weston, 2016; Li et al., 2016; Hancock et al., 2019; Li et al., 2022).”
Training Language Models with Language Feedback at Scale (2023, background): “A few prior works also learn from language feedback for the purpose of ranking candidate outputs rather than generating outputs (Weston, 2016; Li et al., 2016; Hancock et al., 2019; Li et al., 2022; Xu et al., 2022).”