@inproceedings{fu-etal-2026-cwf,
title = "{CWF}: A Collaborative Writing Framework for Personalized and Reliable Long-Form Popular Science Writing",
author = "Fu, Ruibiao and
Tang, Di and
Yang, Yunlong and
Wang, Ran and
Lu, Sicheng and
Wu, Peixuan and
Fan, Xiaoyu and
Ma, Jiacheng and
Luo, HaoZhe and
Xiao, Yang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1495/",
pages = "31820--31844",
ISBN = "979-8-89176-462-0",
abstract = "We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different cognitive levels while preserving factual accuracy. However, improving personalization often introduces simplifications that increase the risk of hallucination and factual distortion. To address these challenges, we first construct a dataset of 39,134 entries and a reader-centric Personalized Science Communication Benchmark (PSCB) that jointly evaluates audience adaptation and factual accuracy. To reduce data and computational requirements while improving generalization across domains and audiences, we introduce DA-MoE, which explicitly decouples audience adaptation from domain knowledge through separate modeling. To enable robust verification and revision in evidence-scarce scenarios, a multi-agent fact-checking mechanism that augments limited evidence with role-specific agent debate and propagates confidence over a graph is proposed. Experiments on PSCB show that our approach achieves state-of-the-art performance."
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%0 Conference Proceedings
%T CWF: A Collaborative Writing Framework for Personalized and Reliable Long-Form Popular Science Writing
%A Fu, Ruibiao
%A Tang, Di
%A Yang, Yunlong
%A Wang, Ran
%A Lu, Sicheng
%A Wu, Peixuan
%A Fan, Xiaoyu
%A Ma, Jiacheng
%A Luo, HaoZhe
%A Xiao, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F fu-etal-2026-cwf
%X We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different cognitive levels while preserving factual accuracy. However, improving personalization often introduces simplifications that increase the risk of hallucination and factual distortion. To address these challenges, we first construct a dataset of 39,134 entries and a reader-centric Personalized Science Communication Benchmark (PSCB) that jointly evaluates audience adaptation and factual accuracy. To reduce data and computational requirements while improving generalization across domains and audiences, we introduce DA-MoE, which explicitly decouples audience adaptation from domain knowledge through separate modeling. To enable robust verification and revision in evidence-scarce scenarios, a multi-agent fact-checking mechanism that augments limited evidence with role-specific agent debate and propagates confidence over a graph is proposed. Experiments on PSCB show that our approach achieves state-of-the-art performance.
%U https://aclanthology.org/2026.findings-emnlp.1495/
%P 31820-31844
Markdown (Informal)
[CWF: A Collaborative Writing Framework for Personalized and Reliable Long-Form Popular Science Writing](https://aclanthology.org/2026.findings-emnlp.1495/) (Fu et al., Findings 2026)
ACL
- Ruibiao Fu, Di Tang, Yunlong Yang, Ran Wang, Sicheng Lu, Peixuan Wu, Xiaoyu Fan, Jiacheng Ma, HaoZhe Luo, and Yang Xiao. 2026. CWF: A Collaborative Writing Framework for Personalized and Reliable Long-Form Popular Science Writing. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 31820–31844, Budapest, Hungary. Association for Computational Linguistics.