@inproceedings{qin-etal-2026-scipedia,
title = "{S}ci{P}edia: Unlocking the Value of Scientific Data for Pre-training",
author = "Qin, Yiwei and
Huang, Zhen and
Mi, Tiantian and
Si, Weiye and
Guo, Qipeng and
Feng, Siyuan and
Liu, Pengfei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2181/",
pages = "47112--47159",
ISBN = "979-8-89176-390-6",
abstract = "High-quality scientific data is critical for advancing LLMs, yet academic literature remains largely underutilized. This work addresses the fundamental question: How can we systematically unlock scientific data{'}s value for pre-training? First, we construct a large-scale raw scientific corpus but identify a critical Learnability Gap, revealing that direct pre-training yields negligible gains. To bridge this, we develop a multi-stage pipeline featuring content cleaning and pedagogical augmentation, resulting in SciPedia, a 900B-token corpus. Finally, we establish a controlled verification framework: we develop SciPedia-Eval benchmark and conduct 600B tokens of continued pre-training (CPT) starting from transparent base models (3B/7B) trained from scratch. Compared to a CPT baseline trained with general-purpose data, our approach with SciPedia data boosts average performance by +2.12 (3B) and +2.95 (7B), reaching +5.60 and +8.40 on in-domain tasks. This setup further allows us to derive empirical guidelines for data composition and model configurations."
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%0 Conference Proceedings
%T SciPedia: Unlocking the Value of Scientific Data for Pre-training
%A Qin, Yiwei
%A Huang, Zhen
%A Mi, Tiantian
%A Si, Weiye
%A Guo, Qipeng
%A Feng, Siyuan
%A Liu, Pengfei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F qin-etal-2026-scipedia
%X High-quality scientific data is critical for advancing LLMs, yet academic literature remains largely underutilized. This work addresses the fundamental question: How can we systematically unlock scientific data’s value for pre-training? First, we construct a large-scale raw scientific corpus but identify a critical Learnability Gap, revealing that direct pre-training yields negligible gains. To bridge this, we develop a multi-stage pipeline featuring content cleaning and pedagogical augmentation, resulting in SciPedia, a 900B-token corpus. Finally, we establish a controlled verification framework: we develop SciPedia-Eval benchmark and conduct 600B tokens of continued pre-training (CPT) starting from transparent base models (3B/7B) trained from scratch. Compared to a CPT baseline trained with general-purpose data, our approach with SciPedia data boosts average performance by +2.12 (3B) and +2.95 (7B), reaching +5.60 and +8.40 on in-domain tasks. This setup further allows us to derive empirical guidelines for data composition and model configurations.
%U https://aclanthology.org/2026.acl-long.2181/
%P 47112-47159
Markdown (Informal)
[SciPedia: Unlocking the Value of Scientific Data for Pre-training](https://aclanthology.org/2026.acl-long.2181/) (Qin et al., ACL 2026)
ACL
- Yiwei Qin, Zhen Huang, Tiantian Mi, Weiye Si, Qipeng Guo, Siyuan Feng, and Pengfei Liu. 2026. SciPedia: Unlocking the Value of Scientific Data for Pre-training. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 47112–47159, San Diego, California, United States. Association for Computational Linguistics.