Rise of Smaller Language Models, Advantages and Limitations

Check here the rise of smaller language models, highlighting their advantages in efficiency, cost-effectiveness, and accessibility. Also Examines how these models are reshaping AI applications and making advanced language processing more scalable.

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Context: Recent advancements in Artificial Intelligence (AI) have seen a shift from scaling up massive models to focusing on smaller, more efficient models.

About Small Language Models (SLMs)

SLMs are artificial intelligence (AI) models that can process, understand and generate human language.
SLMs also work on a neural network-based architecture known as the transformer model.
SLMs are cheaper, faster to train and require fewer computational resources compared to LLMs.

Advantages of SLMs

Accuracy: SLMs can provide higher quality and more accurate results than LLMs.
Privacy: SLMs can perform tasks locally, such as text prediction, voice commands, and translation without sending data to the cloud.
Specialization: SLMs are designed for specific use cases rather than the broad general intelligence goals of LLMs.
Sustainability: SLMs have lower energy consumption, which contributes to improved sustainability.
On-Device Functionality: Apple Intelligence integrates SLMs into iPhones and iPads, balancing performance with device constraints.

Limitations of SLMs

Inferior Performance on Complex Tasks:
- SLMs struggle with benchmarks like coding, logical reasoning and solving intricate problems.
- They cannot yet match LLMs in addressing challenges requiring broad general intelligence.
Factual accuracy: SLMs may not be able to store much factual knowledge, which can lead to factual inaccuracies