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    <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.</abstract>
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%0 Conference Proceedings
%T Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning
%A Chen, Yuhan
%A Zou, Bowei
%A Fan, Yifan
%A Chen, Yuchong
%A Cao, Shujun
%A Hong, Yu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F chen-etal-2025-enhancing-attributed
%X 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.
%R 10.18653/v1/2025.findings-emnlp.420
%U https://aclanthology.org/2025.findings-emnlp.420/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.420
%P 7947-7956

Markdown (Informal)

[Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning](https://aclanthology.org/2025.findings-emnlp.420/) (Chen et al., Findings 2025)

• Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning (Chen et al., Findings 2025)

ACL

• Yuhan Chen, Bowei Zou, Yifan Fan, Yuchong Chen, Shujun Cao, and Yu Hong. 2025. Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 7947–7956, Suzhou, China. Association for Computational Linguistics.