Small Language Models for Application Interactions: A Case Study
This work addresses the challenge of efficient and accurate application interactions for users in cloud supply chain management, but it is incremental as it applies existing SLM methods to a new domain-specific case.
The paper tackled the problem of using Small Language Models (SLMs) for natural language interactions in application usage, specifically in a Microsoft cloud supply chain fulfillment case study, and found that SLMs outperformed larger models in accuracy and running time, even with small fine-tuning datasets.
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.