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Donate > cs > arXiv:2403.13187 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search open search GO open navigation menu quick links Login Help Pages About --> Computer Science > Neural and Evolutionary Computing arXiv:2403.13187 (cs) [Submitted on 19 Mar 2024 ( v1 ), last revised 27 Jan 2025 (this version, v2)] Title: Evolutionary Optimization of Model Merging Recipes Authors: Takuya Akiba , Makoto Shing , Yujin Tang , Qi Sun , David Ha View a PDF of the paper titled Evolutionary Optimization of Model Merging Recipes, by Takuya Akiba and 4 other authors View PDF HTML (experimental) Abstract: Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. While model merging has emerged as a cost-effective promising approach for creating new models by combining existing ones, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models like a Japanese LLM with Math reasoning capabilities. Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese VLMs. This work not only contributes new state-of-the-art models back to the open-source community, but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development. Comments: Authors' submitted version before final edits. Published in Nature Machine Intelligence on January 27, 2025: this https URL Subjects: Neural and Evolutionary Computing (cs.NE) Cite as: arXiv:2403.13187 [cs.NE] (or arXiv:2403.13187v2 [cs.NE] for this version) https://doi.org/10.48550/arXiv.2403.13187 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Nat Mach Intell (2025) Related DOI : https://doi.org/10.1038/s42256-024-00975-8 Focus to learn more DOI(s) linking to related resources Submission history From: Takuya Akiba [ view email ] [v1] Tue, 19 Mar 2024 22:56:53 UTC (1,162 KB) [v2] Mon, 27 Jan 2025 10:19:44 UTC (1,342 KB) Full-text links: Access Paper: View a PDF of the paper titled Evolutionary Optimization of Model Merging Recipes, by Takuya Akiba and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NE < prev | next > new | recent | 2024-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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