Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers
By: Yue Kang, Zhuoyi Huang, Benji Schussheim, Diana Licon, Dina Atia, Shixing Cao, Jacob Danovitch, Kunho Kim, Billy Norcilien, Jonah Karpman, Mahmound Sayed, Mike Taylor, Tao Sun, Pavel Metrikov, Vipul Agarwal, Chris Quirk, Ye-Yi Wang, Nick Craswell, Irene Shaffer, Tianwei Chen, Sulaiman Vesal, Soundar Srinivasan
Published: 2026-01-06
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Abstract
This paper investigates the fine-tuning of small language models to act as efficient enterprise search relevance labelers. The approach demonstrates how smaller LLMs can be optimized for specific business applications, leading to improved search accuracy, reduced operational costs, and democratized access to advanced AI capabilities for a wider range of businesses and use cases.