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.
@article{arxiv.2602.14060,
title = {LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts},
author = {Yang Liu and Jiaye Yang and Weikang Li and Jiahui Liang and Yang Li and Lingyong Yan},
journal= {arXiv preprint arXiv:2602.14060},
year = {2026}
}