Summary of Small Language Models For Application Interactions: a Case Study, by Beibin Li et al.
Small Language Models for Application Interactions: A Case Study
by Beibin Li, Yi Zhang, Sébastien Bubeck, Jeevan Pathuri, Ishai Menache
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates the effectiveness of Small Language Models (SLMs) in enhancing application usage through natural language interactions. The study focuses on a specific internal Microsoft application used for cloud supply chain fulfillment. The findings indicate that small models can outperform larger ones in terms of both accuracy and running time, even when fine-tuned on small datasets. Additionally, the paper highlights design considerations for SLM-based systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Small Language Models (SLMs) can help people use applications better through natural language interactions. The researchers studied a special application used by Microsoft to manage cloud supply chains. They found that small models are just as good or even better than larger ones when it comes to getting the right answers and running quickly, even if they only have a little bit of data to learn from. The paper also talks about what to think about when designing systems that use SLMs. |
