Little Brains, Big Feats: Exploring Compact Language Models
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Computer Science > Computation and Language
Title:Little Brains, Big Feats: Exploring Compact Language Models
Abstract:While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention. In this study, we investigate how smaller language models perform during the generation stage within a Retrieval-Augmented Generation (RAG) system. To benchmark these models effectively, we utilised both open-source and proprietary datasets covering diverse subject areas and question types. Our findings demonstrate that a RAG system with small language models can be executed directly on-device without requiring any GPU hardware within a reasonable time. The experimental code and links to the supplementary materials can be accessed through the GitHub repository: this https URL.
| Comments: | Accepted to ECML PKDD 2026, Applied Data Science track. Author preprint; the definitive version will appear in the proceedings of ECML PKDD 2026, Springer LNCS |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.30062 [cs.CL] |
| (or arXiv:2606.30062v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.30062
arXiv-issued DOI via DataCite (pending registration)
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