Shane Arora, Marzena Karpinska, Hung-Ting Chen, Ipsita Bhattacharjee, Mohit Iyyer, Eunsol Choi. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Hung-Ting Chen, Eunsol Choi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

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.* Equal contribution. 1 The term continual learning is at times used to refer to a scenario where models adapt to new tasks over time.We study improving the model continually on its original task.

Question answering models can use rich knowledge sources -up to one hundred retrieved passages and parametric knowledge in the large-scale language model (LM).Prior work assumes information in such knowledge sources is consistent with each other, paying little attention to how models blend information stored in their LM parameters with that from retrieved evidence documents.In this paper, we simulate knowledge conflicts (i.e., where parametric knowledge suggests one answer and different passages suggest different answers) and examine model behaviors.We find retrieval performance heavily impacts which sources models rely on, and current models mostly rely on non-parametric knowledge in their best-performing settings.We discover a troubling trend that contradictions among knowledge sources affect model confidence only marginally.To address this issue, we present a new calibration study, where models are discouraged from presenting any single answer when presented with multiple conflicting answer candidates in retrieved evidences.