# XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering

Source: [XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering](<https://www.sota2.com/research/paper/68fa71631e209131cea51162>)

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

Data updated: 2025-10-26T02:05:39.468Z

Authors: Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh, Sanjit Batra, Jeffrey Hertzberg, Jaime Murillo, Eran Halperin

Published: 2023-12-06T15:59:06.000Z

## Abstract

Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understanding patient medical history. However, building these systems typically requires expert-annotated QA pairs. Large language models (LLMs), which can perform extractive QA, depend on high quality data in their prompts, specialized for the application domain. We introduce a novel approach, XAIQA, for generating synthetic QA pairs at scale from data naturally available in electronic health records. Our method uses the idea of a classification model explainer to generate questions and answers about medical concepts corresponding to medical codes. In an expert evaluation with two physicians, our method identifies $2.2\\times$ more semantic matches and $3.8\\times$ more clinical abbreviations than two popular approaches that use sentence transformers to create QA pairs. In an ML evaluation, adding our QA pairs improves performance of GPT-4 as an extractive QA model, including on difficult questions. In both the expert and ML evaluations, we examine trade-offs between our method and sentence transformers for QA pair generation depending on question difficulty.

## 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/2312.03567>)

[PDF](<https://arxiv.org/pdf/2312.03567.pdf>)