@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://preview.aclanthology.org/ingest-acl/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."
}