@inproceedings{liu-etal-2026-lm,
    title = "{LM}-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts",
    author = "Liu, Yang  and
      Yang, Jiaye  and
      Li, Weikang  and
      Liang, Jiahui  and
      Li, Yang  and
      Yan, Lingyong",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.1/",
    pages = "1--22",
    ISBN = "979-8-89176-380-7",
    abstract = "We introduce LM-Lexicon, an innovative definition modeling approach that incorporates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. By decomposing the definition modeling task into specialized semantic domains, where small language models are trained as domain experts, LM-Lexicon achieves substantial improvements (+7{\%} BLEU score compared with the prior state-of-the-art model) over existing methods on five widely used benchmarks. Empirically, we demonstrate that 1) the clustering strategy enables fine-grained expert specialization with nearly 10{\%} improvement in definition quality; 2) the semantic-aware domain-level routing mechanism achieves higher expert efficacy (+1{\%}) than conventional token-level routing; and 3) further performance gains can be obtained through test-time compute and semantic expert scaling. Our work advances definition modeling while providing insights into the development of efficient language models for semantic-intensive applications."
}