@inproceedings{peng-etal-2026-hss,
title = "{HSS}-Synth: Humanities and Social Sciences Data Synthesis for {LLM}s",
author = "Peng, Ru and
Zhao, Tianyu and
Gu, Xijun and
Fan, Zhiting and
Xu, Haokai and
Zhang, Jinyang and
Zeng, Yawen and
Zhuang, Yihong and
Yang, Kexin and
Lin, Junyang and
Liu, Dayiheng and
Zhao, Junbo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1880/",
pages = "37706--37732",
ISBN = "979-8-89176-395-1",
abstract = "High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the humanities and social sciences (HSS) are overlooked, and their open-ended nature makes synthesis challenging.Moving beyond prior capability-centric, fragmented attempts, we adopt a subject-centric paradigm, define the first HSS domain system covering 14 mainstream fields, and introduce HSS-Synth{---}the first data synthesis pipeline for HSS.HSS-Synth comprises: (1) constructing seed document from web corpora via multi-step filtering and text refinement evaluated by a judge; (2) specifying ``requirements + persona'' to backtranslate seed document into diverse yet faithful instructions with strict Q{\&}A alignment check; and (3) breaking LLM response limits via teacher-forced Answering that fed seed documents during response to anchor semantics, reduce hallucinations, and preserve tone and integrity.HSS-Synth yields 237k high-quality, diverse instruction-tuning samples that outperform 14 leading baselines on 16 benchmarks. The fine-tuned Qwen3-8B-Base set new SOTA and approached official Qwen3-8B, improving both human preference and knowledge capability without performance seesaws. Extensive experiments demonstrate the HSS-Synth{'}s robustness and transferability.Our code is publicly available at https://github.com/pengr/HSS-Synth."
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<abstract>High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the humanities and social sciences (HSS) are overlooked, and their open-ended nature makes synthesis challenging.Moving beyond prior capability-centric, fragmented attempts, we adopt a subject-centric paradigm, define the first HSS domain system covering 14 mainstream fields, and introduce HSS-Synth—the first data synthesis pipeline for HSS.HSS-Synth comprises: (1) constructing seed document from web corpora via multi-step filtering and text refinement evaluated by a judge; (2) specifying “requirements + persona” to backtranslate seed document into diverse yet faithful instructions with strict Q&A alignment check; and (3) breaking LLM response limits via teacher-forced Answering that fed seed documents during response to anchor semantics, reduce hallucinations, and preserve tone and integrity.HSS-Synth yields 237k high-quality, diverse instruction-tuning samples that outperform 14 leading baselines on 16 benchmarks. The fine-tuned Qwen3-8B-Base set new SOTA and approached official Qwen3-8B, improving both human preference and knowledge capability without performance seesaws. Extensive experiments demonstrate the HSS-Synth’s robustness and transferability.Our code is publicly available at https://github.com/pengr/HSS-Synth.</abstract>
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%0 Conference Proceedings
%T HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs
%A Peng, Ru
%A Zhao, Tianyu
%A Gu, Xijun
%A Fan, Zhiting
%A Xu, Haokai
%A Zhang, Jinyang
%A Zeng, Yawen
%A Zhuang, Yihong
%A Yang, Kexin
%A Lin, Junyang
%A Liu, Dayiheng
%A Zhao, Junbo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F peng-etal-2026-hss
%X High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the humanities and social sciences (HSS) are overlooked, and their open-ended nature makes synthesis challenging.Moving beyond prior capability-centric, fragmented attempts, we adopt a subject-centric paradigm, define the first HSS domain system covering 14 mainstream fields, and introduce HSS-Synth—the first data synthesis pipeline for HSS.HSS-Synth comprises: (1) constructing seed document from web corpora via multi-step filtering and text refinement evaluated by a judge; (2) specifying “requirements + persona” to backtranslate seed document into diverse yet faithful instructions with strict Q&A alignment check; and (3) breaking LLM response limits via teacher-forced Answering that fed seed documents during response to anchor semantics, reduce hallucinations, and preserve tone and integrity.HSS-Synth yields 237k high-quality, diverse instruction-tuning samples that outperform 14 leading baselines on 16 benchmarks. The fine-tuned Qwen3-8B-Base set new SOTA and approached official Qwen3-8B, improving both human preference and knowledge capability without performance seesaws. Extensive experiments demonstrate the HSS-Synth’s robustness and transferability.Our code is publicly available at https://github.com/pengr/HSS-Synth.
%U https://aclanthology.org/2026.findings-acl.1880/
%P 37706-37732
Markdown (Informal)
[HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs](https://aclanthology.org/2026.findings-acl.1880/) (Peng et al., Findings 2026)
ACL
- Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, and Junbo Zhao. 2026. HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 37706–37732, San Diego, California, United States. Association for Computational Linguistics.