Rising Stars: Unveiling the Power and Potential of Small Language Models in Modern AI

divyarakesh
Jan 19, 2024
2 min read

Introduction

Indeed, large language models (LLMs) like GPT-3, BERT, T-5, and XLNet have truly revolutionized various industries. These models excel at understanding context and producing coherent text. However, it's important to note that even with their continued development and progress, LLMs do have their limitations.

On the other hand, specialized language models (SLMs) are designed for specific industries or tasks, delivering more accurate and relevant results. They bring substantial advantages in business-specific scenarios. SLMs show impressive reasoning and language comprehension skills, performing well on various benchmarks.

Large Language Models (LLMs)

Advantage	Limitations
LLMs have the ability to understand context and produce coherent text.	LLMs may produce incorrect or biased information if trained on biased data.
They can generate large amounts of content quickly, saving time and effort.	They can generate plausible but false information, leading to misinformation.
LLMs can be trained on vast amounts of data, allowing them to have a broad knowledge base	LLMs require significant computational resources and energy to train and run

Small Language Models (SLMs)

Small Language Models (SLMs) are compact versions of large-scale language models that are designed to perform various natural language processing tasks. They use following techniques to make it smaller and work better.

Knowledge Distillation : This approach transfers knowledge from a pre-trained LLM to a smaller model, distilling its essential capabilities while reducing complexity.
Pruning and Quantization : Using these techniques involves removing unnecessary parts of the model and reducing the precision of its weights. This helps to decrease the model's size and resource requirements even more
Efficient Architectures : Ongoing research is dedicated to developing new architectures specifically designed for SLMs, with a focus on improving performance and efficiency.

Challenges with Large Language Models (LLMs)	How SLMs solve the Challenges
Too much context: LLMs often generate responses that are overly verbose and include unnecessary information, making it difficult to extract relevant insights	Focused context: SLMs provide more control over the context, allowing for concise and targeted responses that address specific needs
Data bias: LLMs can perpetuate biases present in the training data, leading to biased outputs and potentially harmful consequences	Reduced bias: SLMs can be trained on focussed datasets and incorporate fairness measures to mitigate biases, resulting in more equitable outputs
Computational power requirements: LLMs require significant computational resources to train and deploy, making them inaccessible for many organizations with limited resources	Lower computational requirements: SLMs are more resource-efficient, making them accessible to a wider range of organizations and applications.

Challenges with SLMs

Handling Complex Cases : Small language models (SLMs) often struggle with handling complex cases and may provide inaccurate or incomplete responses.
Better Few-Shot Generalization : Large language models (LLMs) have an advantage in providing better few-shot generalization, meaning they can generate more accurate and relevant responses with limited training examples

Examples of Small Language Models (SLMs):

Hugging Face's DistilBERT: A compact version of BERT with fewer parameters, suitable for various natural language processing tasks.
Microsoft's Phi-2: A small language model designed for efficient inference on edge devices, capable of handling real-time language processing tasks.
Mistral models: Lightweight language models developed by Mistral, optimized for low-resource environments and quick response times.
Llama models: Small language models created by Llama Labs, designed for efficient language understanding and generation tasks.
Cohere models: Compact language models developed by Cohere Technologies, suitable for a wide range of natural language processing applications.
AI21 Labs models: Small language models created by AI21 Labs, known for their advanced language understanding and generation capabilities