SLMs vs LLMs in AI : Whats the Buzzzz ?

[Chetan_Tiwary_]

Ok, what are the Language Models and why do AI need it ?

Simple - because Humans understand Natural Language and Machine understands Binary Code !!

Language models are essential for AI because they act as a crucial translator, bridging the gap between seamless human-machine interaction.

They facilitate efficient self-supervised learning by automatically generating training labels from vast amounts of raw text, removing the dependency on costly manual annotation. By quantifying text data, these models empower AI systems to analyze large text repositories, uncover trends, detect anomalies, and extract valuable insights, which is vital for data-driven decision-making across diverse sectors like healthcare, finance, and legal.

So - in a nutshell -  by teaching computers the nuances, statistics, and structure of language, language models form an indispensable core that underpins modern AI's ability to effectively interact with, interpret, and create natural language content in nearly every contemporary AI-driven application.

Now back to the topic : SLM or LLM

Well, to be as simple as possible - both are language models used to train your AI !

Both LLMs and SLMs are trained on data sets consisting of language, which distinguishes them from models trained on images or videos !

Small Language Models (SLMs) are particularly valuable when prioritizing efficiency, cost-effectiveness, and specialized domain tasks, allowing organizations to implement language understanding capabilities on devices with limited resources or for specific functions without needing extensive infrastructure.

To compare, Large Language Models (LLMs) are designed for broader applications, excelling in areas requiring extensive generalization, creative content generation, and sophisticated linguistic analysis, making them suitable for complex tasks like powering advanced chatbots or writing tools, though they demand significant computational power, memory, and data resources.

The Catch :

Why Small Language Models (SLMs) Are Needed:

Lower Resource Needs (Compute/Memory) >> Faster Performance (Inference/Real-time) >> Cost-Effective (Training/Hosting) >> Domain Specific Accuracy (Fine-tuning) >> Enhanced Data Privacy (On-prem Deployment)

example use case - a health/hospital app chatbot which can answer on health related general queries .

Why Large Language Models (LLMs) Are Needed:

Broad Knowledge & Generalization >> Creative Content Generation >> Complex Reasoning & Context >> Adapts to New Tasks Quickly (Few/Zero-shot) >> Versatile Use Cases (Single Model) >> Handles Diverse Topics

example use case : ChatGPT which can answer anything asked in general.

****************************************

Now, as you already know - Training any model for a business use case, whether LLM or SLM, is a resource-intensive process. But do you know that model training and model inference is not the same resource hungry ?

The primary distinction between model training and model inference lies in their sequence and purpose: training is the initial phase of developing an AI model, while inference is the subsequent process where that now-trained model is used to generate predictions or make decisions when presented with new, previously unseen data.

When it comes to making inferences, LLMs like the Granite family require substantial resources, that of a highh end GPU server whereas SLMs are specifically designed to perform inference efficiently using the limited resources available on devices like smartphones.

Now based on these information and a little common sense - do I have to mention the limitation and security risks of LLM vs SLM - isnt it obvious ?

Food for thought -

1. Which of them do you think pose risk of data leakage / exposure ?

2. Which of them do you think has a bias and narrow knowledge set ?

see - it was not that difficult to answer these 2 questions - and hence AI will not replace humans :xD!!