@inproceedings{tsunokake-etal-2026-micro,
    title = "Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks",
    author = "Tsunokake, Masaya  and
      Koreeda, Yuta  and
      Morishita, Terufumi  and
      Nagatsuka, Koichi  and
      Tomonari, Hikaru  and
      Sogawa, Yasuhiro",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.22/",
    pages = "304--316",
    ISBN = "979-8-89176-384-5",
    abstract = "When applying LLMs to real-world enterprise operations, LLMs need to handle proprietary knowledge in small domains of specific operations (micro domains).A previous study shows micro domain-adaptive pre-training (mDAPT) with fewer documents is effective, similarly to DAPT in larger domains.However, it evaluates mDAPT only on multiple-choice questions; thus, its effectiveness for generative tasks in real-world operations remains unknown.We aim to reveal the potential and bottlenecks of mDAPT for generative tasks.To this end, we disentangle the answering process into three subtasks and evaluate the performance of each subtask: (1) eliciting facts relevant to questions from an LLM{'}s own knowledge, (2) reasoning over the facts to obtain conclusions, and (3) composing long-form answers based on the conclusions.We verified mDAPT on proprietary IT product knowledge for real-world questions in IT technical support operations. As a result, mDAPT resolved the elicitation task that the base model struggled with but did not resolve other subtasks.This clarifies mDAPT{'}s effectiveness in the knowledge aspect and its bottlenecks in other aspects.Further analysis empirically shows that resolving the elicitation and reasoning tasks ensures sufficient performance (over 90{\%}), emphasizing the need to enhance reasoning capability."
}