Shortcomings of Question Answering Based Factuality Frameworks for Error Localization

• URL: http://arxiv.org/abs/2210.06748v1
• Date: Thu, 13 Oct 2022 05:23:38 GMT
• Title: Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
• Authors: Ryo Kamoi, Tanya Goyal, Greg Durrett
• Abstract summary: We show that question answering (QA)-based factuality metrics fail to correctly identify error spans in generated summaries. Our analysis reveals a major reason for such poor localization: questions generated by the QG module often inherit errors from non-factual summaries which are then propagated further into downstream modules. Our experiments conclusively show that there exist fundamental issues with localization using the QA framework which cannot be fixed solely by stronger QA and QG models.
• Score: 51.01957350348377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most popular of which are question answering (QA)-based factuality metrics. These have been shown to work well at predicting summary-level factuality and have potential to localize errors within summaries, but this latter capability has not been systematically evaluated in past research. In this paper, we conduct the first such analysis and find that, contrary to our expectations, QA-based frameworks fail to correctly identify error spans in generated summaries and are outperformed by trivial exact match baselines. Our analysis reveals a major reason for such poor localization: questions generated by the QG module often inherit errors from non-factual summaries which are then propagated further into downstream modules. Moreover, even human-in-the-loop question generation cannot easily offset these problems. Our experiments conclusively show that there exist fundamental issues with localization using the QA framework which cannot be fixed solely by stronger QA and QG models.

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