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Large language overkill: How SLMs can beat their bigger, resource-intensive cousins
- Specialized Language Models (SLMs) could play a larger, complementary role in enterprise IT by supplementing large language models (LLMs). SLMs are perfect for performing specialized work that needs more accuracy, consistency, and transparency than LLMs. Unlike LLMs, they are trained on domain-specific data, so they have contextual intelligence to deliver more consistent, predictable, and relevant responses.
- LLMs are not specifically designed for general-purpose tasks, which leads to their tendency to make errors in certain specialized professions such as healthcare, legal, and financial services that require high levels of accuracy. Overreliance on LLMs can lead to complacency and have significant financial consequences or life-or-death repercussions.
- SLMs are better suited to address the limitations of LLMs. They are more explainable, can perform faster, and offer businesses more control over data privacy and security, especially if they are used internally or built specifically for the enterprise. SLMs are developed with a narrower focus and trained on domain-specific data, which gives them contextual intelligence to deliver more consistent, predictable, and relevant responses.
- LLMs and SLMs are not mutually exclusive. In practice, SLMs can augment LLMs, creating hybrid solutions where LLMs provide broader context and SLMs ensure precise execution. It is also essential to have a clear understanding of what use cases to tackle and the necessary skills required to train, fine-tune, and test SLMs.
- AJ Sunder, a co-founder, CIO, and CPO at Responsive, advises technology leaders to continue exploring the possibilities of LLMs. While LLMs can scale problem well, SLMs may not transfer well to certain use cases. Therefore, vetting partners and starting small, testing often, and building on early successes are crucial.
- Investing in SLMs gives companies the opportunity to future-proof their AI strategies, ensure their tools drive innovation, meet the demands of trust, reliability, and control, and stay relevant in today's world.
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