@inproceedings{kim-etal-2026-tellme,
    title = "{TELLME}: Test-Enhanced Learning for Language Model Enrichment",
    author = "Kim, Minjun  and
      Won, Inho  and
      Lim, HyeonSeok  and
      Kim, MinKyu  and
      Yuk, Junghun  and
      Go, Wooyoung  and
      Park, Jongyoul  and
      Park, Jungyeul  and
      Lim, KyungTae",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.84/",
    pages = "1655--1677",
    ISBN = "979-8-89176-386-9",
    abstract = "Continual pre-training (CPT) has been widely adopted as a method for domain expansion in large language models. However, CPT has consistently been accompanied by challenges, such as the difficulty of acquiring large-scale domain-specific datasets and high computational costs. In this study, we propose a novel method called Test-Enhanced Learning for Language Model Enrichment (TELLME) to alleviate these issues. TELLME leverages the Test-Enhanced Learning (TEL) principle, whereby the model{'}s learning efficiency is improved using quizzes during training. It integrates this principle with CPT, thereby promoting efficient domain-specific knowledge acquisition and long-term memory retention. Experimental results demonstrate that TELLME outperforms existing methods by up to 23.6{\%} in the financial domain and achieves a 9.8{\%} improvement in long-term memory retention."
}