The article discusses the contrasting roles of large language models (LLMs) and smaller language models (SLMs) in the development of generative AI applications. While LLMs have garnered significant attention and investment, the emergence of SLMs offers companies cost-effective alternatives. SLMs, trained on smaller datasets, are less resource-intensive, enabling their use in various environments, including mobile devices. The ease of retraining SLMs allows developers to keep applications updated with recent information, making them potentially more adaptive and efficient in processing user queries compared to LLMs, which often require substantial resources and complex architectures.
Large language models (LLMs) and small language models (SLMs) will have distinct roles in the generative AI space, ensuring cost-effective applications.
While LLMs have dominated funding and innovation, small language models (SLMs) present viable alternatives, trained on specific datasets at lower costs.
SLMs are more adaptable, capable of operating in resource-constrained environments, making them suitable for edge computing and mobile devices.
Retaining the ability to retrain SLMs easily allows for timely updates to applications, enhancing their relevance and applicability in dynamic scenarios.
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