小腦袋，大成就：探討緊湊型語言模型

摘要

近年来，大型语言模型在研究领域占据主导地位，但小型语言模型在多个领域中仍保持高度相关性，然而它们受到的关注却少得多。本研究探讨了较小型的语言模型在检索增强生成（RAG）系统生成阶段的表现。为有效评估这些模型，我们采用了涵盖不同学科领域及问题类型的开源与专有数据集。研究结果表明，配备小型语言模型的RAG系统可直接在设备端运行，无需任何GPU硬件，且能在合理时间内完成处理。实验代码及补充材料链接可通过GitHub仓库获取：https://github.com/SibNN/SLM-RAG-EVAL。

English

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.