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    <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.</abstract>
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%0 Conference Proceedings
%T LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts
%A Liu, Yang
%A Yang, Jiaye
%A Li, Weikang
%A Liang, Jiahui
%A Li, Yang
%A Yan, Lingyong
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F liu-etal-2026-lm
%X 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.
%U https://aclanthology.org/2026.eacl-long.1/
%P 1-22

Markdown (Informal)

[LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts](https://aclanthology.org/2026.eacl-long.1/) (Liu et al., EACL 2026)

• LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts (Liu et al., EACL 2026)

ACL

• Yang Liu, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li, and Lingyong Yan. 2026. LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1–22, Rabat, Morocco. Association for Computational Linguistics.