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Small Language Models for Application Interactions: A Case Study

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arxiv 2405.20347 v1 pith:NNVHWFWO submitted 2024-05-23 cs.CL cs.AIcs.LG

Beibin Li , Yi Zhang , Sébastien Bubeck , Jeevan Pathuri , Ishai Menache This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords smallapplicationlanguagemodelsinteractionsaccuracyalongsidecase
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We study the efficacy of Small Language Models (SLMs) in facilitating application usage through natural language interactions. Our focus here is on a particular internal application used in Microsoft for cloud supply chain fulfilment. Our experiments show that small models can outperform much larger ones in terms of both accuracy and running time, even when fine-tuned on small datasets. Alongside these results, we also highlight SLM-based system design considerations.

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Cited by 1 Pith paper

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  1. Large Language Models for Supply Chain Decisions

    cs.AI 2025-07 conditional novelty 4.0 of 10

    Microsoft used GPT-4 to translate supply chain planners' natural-language questions into optimization model changes, reporting roughly 90 percent accuracy and 23 percent time savings without disclosing evaluation data.

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