# F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering

Source: [F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering](<https://www.sota2.com/research/paper/68fa687e1e209131cea1c5a0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:04:44.961Z

Authors: Hendrik Schuff, Heike Adel, Ngoc Thang Vu

Published: 2020-10-13T10:53:20.000Z

## Abstract

Explainable question answering systems predict an answer together with an explanation showing why the answer has been selected. The goal is to enable users to assess the correctness of the system and understand its reasoning process. However, we show that current models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling. We conduct experiments on the HOTPOTQA benchmark data set and perform a user study. The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users. Our scores are better aligned with user experience, making them promising candidates for model selection.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.06283>)

[PDF](<https://arxiv.org/pdf/2010.06283.pdf>)