What are differences between Small Language Models (SLMs) and Large Language Models?
Size:
Small Language Models (SLMs) are characterized by a limited data capacity during training phases – typically ranging from 1-7 billion parameters, compared to LLMs which can have 300B+ parameters.
Use Cases and Interactions with Language:
Another primary difference lies in how SLMs and LLMs interact with language. SLMs excel at executing specific tasks within a confined scope of understanding due to their focused design – akin to a specialized athlete who excels in one sport but won’t necessarily be strong at other sports.
On the other hand, Large Language Models are more akin to a versatile athlete capable of performing multiple sports effectively, yet with varying degrees of proficiency. LLMs offer an impressive ability to generalize language understanding and generate content, but they often fall short when required to operate within specialized or highly domain-specific contexts (where SLMs might outperform them).