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Small Language Models Prioritize Privacy and Efficiency

AI | May 31, 2024

Small Language Models Quality vs size Microsoft - Small Language Models Prioritize Privacy and Efficiency

Graphic illustrating how the quality of new Phi-3 models, as measured by performance on the Massive Multitask Language Understanding (MMLU) benchmark, compares to other models of similar size. (Image courtesy of Microsoft)

Small Language Models: Efficiency and Privacy with Big Potential

The use of small language models (SLMs) in AI advancements is growing and becoming more popular among developers and enterprises due to their distinct benefits over their larger counterparts in terms of cost, efficiency, and privacy.  Prominent tech companies, such as Microsoft, are allocating resources towards the development of SLMs, which is promising in terms of their potential future impact across virtually all domains.

The trend toward smaller language models is consistent with more general developments in AI, such as the shift from cloud-based to on-device AI processing. The desire for decreased latency, enhanced privacy, and lower prices is what is driving this shift.  SLMs are anticipated to close the gap between open and proprietary models as they develop, providing flexible options for businesses and consumers alike.

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Sonali Yadav, principal product manager for Generative AI at Microsoft:

“What we’re going to start to see is not a shift from large to small, but a shift from a singular category of models to a portfolio of models where customers get the ability to make a decision on what is the best model for their scenario.”

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1.  Cost Effectiveness Performance

Small language models, such as the Phi-3-mini from Microsoft, provide significant performance using a small percentage of the parameters compared to big language models (LLMs). With 3.8 billion parameters, Phi-3-mini performs better than models twice its size.  Due to SLMs' decreased computational requirements, AI is now more affordable for startups and smaller companies.

2.  Applications for On-Device

Applications that need to be offline and have low latency should use SLMs. By handling data locally, they can be installed on gadgets such as smart sensors, cellphones, and agricultural machinery, guaranteeing data privacy.

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Because on-device processing enables real-time AI applications without relying on cloud infrastructure, it is especially helpful in places with poor internet connectivity, such rural areas.

3.  Improved Security and Privacy

SLMs reduce privacy risks by storing data locally on the device rather than sending sensitive data to cloud servers. They are therefore appropriate for regulated industries with strict requirements for data privacy.  The responsible deployment of SLMs is ensured by Microsoft's multi-layered approach to AI safety, which includes strict data curation and ethical norms.

4.  Innovative Training

The usage of premium datasets like "TinyStories", together with other cutting-edge training methods from Microsoft, has greatly improved SLM performance. Because these datasets are carefully selected to guarantee both educational value and high-quality information, smaller models are better equipped to handle challenging tasks.


SLMs are expected to become increasingly important in a wide range of applications, from personal devices to industrial automation, as technology advances and use cases prioritize accessibility, privacy, and efficiency.

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They are a promising option for AI in the future because of their capacity to provide excellent performance with reduced resource requirements.

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