| title | Little Brains, Big Feats: Exploring Compact Language Models |
|---|---|
| authors | Dari Baturova, Elena Bruches, Ivan Chernov, Roman Derunets, Arsenii Fomin, Andrey Kostin |
| date | 2026-06-29 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.30062 |
| affiliation | Siberian Neuronets LLC |
| upvotes | 15 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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: https://github.com/SibNN/SLM-RAG-EVAL. |
cite
@misc{baturova2026little,
title={Little Brains, Big Feats: Exploring Compact Language Models},
author={Dari Baturova and Elena Bruches and Ivan Chernov and Roman Derunets and Arsenii Fomin and Andrey Kostin},
year={2026},
eprint={2606.30062},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.30062}
}