As we embrace the phenomenal power and offerings of AI, many of us wonder about the underlying support. The most common thing which comes to mind is LLM.
LLM’s or Large Language models are the brain behind the AI offerings available to the user. LLM’s are trained on huge amounts of data, complex algorithms to enable them to support the AI offerings and services. These models are trained on various aspects like recognizing the patterns, structures, relationship to support the features like text generation, questions and answers etc. LLMs have been the pillars of AI, mesmerizing all of us with their ability to generate quality text and perform complex tasks.
However, LLM’s are (as the name goes) large, pretty complex, and need lots of effort and training data. But, what about scenarios, where you don’t have access to a supercomputer or a team of AI skilled members? These are the scenarios where small language models (SLMs), the unsung heroes in the AI world, come into picture.
So, what are SLM’s?
Like they say, “all good things come in small packages”, SLM’s are the smaller version of LLM with similar features but trained on smaller data set when compared to LLM’s. They are artificial neural networks trained on a substantial amount of text data, but with fewer parameters (the building blocks of the model) compared to LLM’s. This makes SLM’s leaner, faster, and requiring less computational resources.
Salient Features of SLMs:
Smaller in Size: While the LLMs comprise of trillions of parameters, SLMs have a significantly smaller parameter count, typically in the billions or even millions.
Adaptability: SLMs can be readily updated and improved with new data, making them adaptable to changing requirements.
Lower Latency: The smaller size of the SLM’s helps with faster processing times, enabling real-time applications like chatbots and data analysis.
Targeted Content Generation: SLMs can be fine-tuned to generate specific types of content, like product descriptions or social media posts, for various domains
Targeted Training: rather than being trained on massive datasets covering all the aspects of the language, SLMs are fine-tuned for specific domains or tasks. This allows them to be experts in such areas.
Advantages of SLMs:
Efficiency: SLM’s are faster to train, run, and deploy, making them more cost-effective solutions, especially for projects with resource crunch. They perform well on a lot of different types of tasks using minimal resources, allowing them to execute on devices like smartphones, making them a go to option for the mobile/on-the-go applications.
Accessibility: Developing and deploying LLMs requires significant expertise and infrastructure. SLMs, are more accessible to a wider range of developers and businesses due to their lower resource requirements allowing for integration into various devices and applications, thus increasing the reach of the AI capabilities to a wider audience.
Customization: Due to their smaller size, SLMs can be more easily fine-tuned for specific tasks and domains. The targeted training allows for fine-tuning to specific domains and tasks, which provides a potentially better performance. This facilitates the creation of AI solutions tailored to specialized segment of the market and their unique requirements.
Privacy: Training SLMs on smaller datasets potentially offer better control over PII data, helping to address concerns related to privacy.
Real-Time Applications: Due to their high speed, it makes the SLM’s ideal for real-time scenarios where response time is crucial.
Disadvantages of SLMs:
Limited Capability: While SLMs can handle many tasks, they still lag in terms of capabilities which are not as extensive as LLMs. SLM’s may not fit in case of complex reasoning or language understanding which needs to consider subtle details as well.
Lower Accuracy: Due to the smaller training datasets, SLMs may not achieve the same level of accuracy as LLMs on complex tasks.
Data Dependence: to be effective the SLM depends heavily on the quality and quantity of data it’s trained on. Insufficient data would potentially lead to biases or impact performance.
Conclusion:
SLMs offer a viable option compared to LLMs, especially for those use cases which required an efficient and accessible AI solution. While SLM’s may not be as powerful as LLM’s, their strengths in speed, customization, and practical use cases make them invaluable for a wide range of applications. As AI technology continues to evolve, SLMs will play an important role in making AI accessible to “one and all” and bringing its power to more and more users.
