Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning
Yuhan Chen, Bowei Zou, Yifan Fan, Yuchong Chen, Shujun Cao, Yu Hong
Abstract
We study Attributed Question Answering (abbr., AQA), a newly-released long-form answer generation task. The tailored and efficient training programmes haven’t yet been leveraged to strengthen AQA models. This hinders the simultaneous enhancement of their essential capabilities, including evidence identification, cross-source relation recognition and anti-distraction reasoning. To address the issue, we propose a tailored progressive curriculum learning approach, and use it to optimize both encoder-decoder and decoder-only AQA models. Experiments on the benchmark QuoteSum show that our approach yields substantial improvements and enables the AQA performance to reach 73.9% Sem-F1 score.
BibTeX
@inproceedings{emnlp2025_enhancingattribu,
title = {Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning},
author = {Yuhan Chen and Bowei Zou and Yifan Fan and Yuchong Chen and Shujun Cao and Yu Hong},
booktitle = {EMNLP 2025},
year = {2025}
}