Companies like Microsoft, AT&T, and NoBroker are already experimenting with SLMs, using them for specific applications such as customer service and sentiment analysis. The future of SLMs looks promising, with trends like on-device AI, multimodal functionality, and personalized AI experiences. These developments enable offline capabilities, enhance privacy, and offer hyper-personalized user experiences. As enterprises explore AI possibilities, SLMs provide a tailored, efficient, and affordable solution, although navigating this rapidly advancing technology can be complex.
Key takeaways:
- Small Language Models (SLMs) offer a cost-effective and efficient alternative to Large Language Models (LLMs) for specific business applications.
- SLMs minimize issues like bias and hallucinations by training on curated, domain-specific datasets, making them suitable for tasks where accuracy is crucial.
- SLMs can be quickly fine-tuned and updated, allowing them to adapt to dynamic business environments and maintain data privacy when deployed in private data centers.
- Future trends for SLMs include on-device AI, multimodal functionality, and personalized AI experiences, expanding their potential applications.