Enterprises grapple with the significant challenge of managing proprietary
unstructured data, hindering efficient information retrieval. This has led to
the emergence of AI-driven information retrieval solutions, designed to adeptly
extract relevant insights to address employee inquiries. These solutions often
leverage pre-trained embedding models and generative models as foundational
components. While pre-trained embeddings may exhibit proximity or disparity
based on their original training objectives, they might not fully align with
the unique characteristics of enterprise-specific data, leading to suboptimal
alignment with the retrieval goals of enterprise environments. In this paper,
we propose a methodology to fine-tune pre-trained embedding models specifically
for enterprise environments. By adapting the embeddings to better suit the
retrieval tasks prevalent in enterprises, we aim to enhance the performance of
information retrieval solutions. We discuss the process of fine-tuning, its
effect on retrieval accuracy, and the potential benefits for enterprise
information management. Our findings demonstrate the efficacy of fine-tuned
embedding models in improving the precision and relevance of search results in
enterprise settings.

本研究提出了一种专门针对企业环境进行微调的预训练嵌入模型的方法，通过使嵌入模型更适合企业中普遍存在的检索任务，旨在提高信息检索解决方案的性能，研究结果表明微调嵌入模型在改善企业环境下搜索结果的精确性和相关性方面是有效的。