Small Language Models for Application Interactions: A Case Study
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909213548085248 |
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| author | Li, Beibin Zhang, Yi Bubeck, Sébastien Pathuri, Jeevan Menache, Ishai |
| author_facet | Li, Beibin Zhang, Yi Bubeck, Sébastien Pathuri, Jeevan Menache, Ishai |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_20347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Small Language Models for Application Interactions: A Case Study Li, Beibin Zhang, Yi Bubeck, Sébastien Pathuri, Jeevan Menache, Ishai Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Small Language Models for Application Interactions: A Case Study |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.20347 |