Small Language Models (SLMs)

SLMs compared to their bigger cousins LLMs are smaller in size, but still have a few billion parameters.

If an LLM is Wikipedia, an SLM is a pocket dictionary.

SLMs can be fine tuned for a specific task and focus on only that task. For example consider a 10th grade exam, who will you choose to take the exam. A 10th grader or a graduate. A graduate may have a broader knowledge than a 10th grader but that is not required to pass a 10th grade exam. A 10th grade student is more than enough. In the enterprise context if we are building a chat bot to answer domain specific questions a SLM will be more than enough than a LLM.

What are the advantages of SLM?

Tailored Efficiency and Precision : SLMs are designed to serve more specific, often niche, purposes, allowing for a level of precision and efficiency that general-purpose LLMs struggle to achieve.

Speed : Their smaller size allows for lower latency in processing requests, making them ideal for AI customer service, real-time data analysis, and other applications where speed is of the essence.

Cost : The smaller size of SLMs translates directly into lower computational and financial costs. Training data, deploying, and maintaining an SLM is considerably less resource-intensive, making it a viable option for smaller enterprises or specific use cases.

But, how does SLMs function well with fewer parameters?

Training Methods:

○ Transfer Learning: Leveraging pre-existing knowledge enables SLMs to adapt and perform efficiently for specific tasks.

○ Knowledge Distillation: Distilling knowledge from LLMs into SLMs allows for comparable performance while reducing computational requirements.

Domain-Specific Adaptation:

○ Tailored to specific domains by training on specific datasets, enhancing effectiveness for specialized tasks.

○ eg: NTG’s SLM excel in understanding construction HSE terminology and making accurate analysis.

Effectiveness Factors:

○ The effectiveness of an SLM depends on its training, fine-tuning process, and task specificity.

○ While SLMs can outperform LLMs in certain scenarios, they may not always be the optimal choice for every application.

Differences between LLMs and SLMs

SLM Examples