Rise of the SLM (Small Language Model)

The integration of Language Models into our daily lives is gaining significant traction. From enhancing workplace productivity to assisting with gym workout routines, the applications are diverse. However, like most technologies, there isn’t a universal solution that fits all scenarios. For example, we don’t use light waves to send messages or rely on wifi to heat our food. As Language Models increasingly permeate our daily existence, we are fine-tuning this technology to suit specific needs. This adaptation has led to the growing prominence of Small Language Models (SLMs).

Impacts of LLM:

Energy consumption, hallucinating, and data security

Large Language Models have made a considerable impact on our world, but not without challenges. They are notably resource-intensive, requiring significant computational power and energy. To illustrate, training a single LLM is estimated to use approximately the same amount of power as 1,000 US households for an entire year.

Moreover, due to their training on extensive data sets, LLMs are more susceptible to “hallucinations” — confidently providing incorrect answers based on gaps in their training.

There are also concerns regarding security and data privacy. Enterprises may hesitate to use LLMs owned by private corporations, fearing access to their sensitive proprietary data.

The emergence of Small Language Models aims to address these issues, offering a more tailored and efficient approach to leveraging language technology.

Why The Transition?

Less energy intensive, smaller training datasets, explainability, data security

Operating at substantially lower costs, less computational requirements, and decreased memory requirements, SLMs demonstrate their effectiveness in environments where computational resources are scarce. They can even be deployed on the edge — leveraging the power of mobile and other IoT devices — making them a more flexible solution for a variety of scenarios.

One of the key advantages of SLMs is their simpler architecture and smaller training datasets, making them more explainable than LLMs. This explainability is crucial for enterprises that need to be accountable for their AI outcomes and for researchers who seek to understand model behavior more effectively.

SLMs are particularly adept at handling task-specific use cases. Their compact size allows for easier customization through training data and fine-tuning to meet specific user needs. They also provide the enterprise the ability to run the entire model either in the cloud or on premise, reducing data privacy insecurity and providing the enterprise more hands on control over the model data. This is especially pressing for industries such as healthcare that have heavy compliance restrictions.

However, despite their advantages, SLMs face challenges. Their smaller, more explainable architecture leads to a more limited context comprehension compared to LLMs, which may result in less accurate responses. Ongoing research aims to address these performance issues. In the meantime, it is essential for users and enterprises to weigh the trade-offs between SLMs and LLMs and decide which model best suits their needs.

If you’re interested in further discussing SLMs or have any questions reach out on LinkedIn!

A Message from AI Mind

Thanks for being a part of our community! Before you go:

• 👏 Clap for the story and follow the author 👉
• 📰 View more content in the AI Mind Publication
• 🧠 Improve your AI prompts effortlessly and FREE
• 🧰 Discover Intuitive AI Tools