Modular Expert Merging for Biomedical Retrieval

Abstract:Adapting general-purpose LLMs into domain-specialized dense retrievers typically requires large-scale training on mixed-domain data. We show that merging independently trained domain-specialized experts consistently exceeds this approach across four decoder-only LLM families (0.6B-7B), four merging methods, and twelve medical and general retrieval tasks from MTEB, suggesting that parameter-space composition captures complementary domain strengths that large-scale mixed-domain training averages out. To further maximize expert quality, we introduce Synthesize-Train-Merge (STM), a modular framework that synthesizes hard negatives with a top-tier LLM and fine-tunes domain-specialized experts via LoRA before merging them, without continual pre-training. Synthesized hard negatives yield the largest gains for smaller models, and STM achieves strong performance on biomedical retrieval tasks while maintaining competitive general-domain results across all four backbone families.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2602.04731 [cs.CL]

(or arXiv:2602.04731v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2602.04731 Focus to learn more arXiv-issued DOI via DataCite

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