What are Small Language Models (SLMs)?

Blog By: Priyanka Rana

What are Small Language Models (SLMs)?

In the world of artificial intelligence, the prevailing notion has been that larger language models, with their vast number of parameters and extensive training data, are the key to unlocking more advanced and capable AI systems.

However, the emergence of small language models (SLMs) is challenging this ‘bigger is better’ mantra, proving that compact models can deliver impressive performance in specific tasks while being more resource-efficient and accessible.

What are Small Language Models?

Small language models are AI language models that contain significantly fewer parameters compared to their larger counterparts, such as GPT-4 or Gemini Advanced.

While large language models (LLMs) boast hundreds of billions of parameters, SLMs typically range from a few million to a few billion parameters. Despite their smaller size, SLMs are demonstrating remarkable effectiveness in specialized tasks and resource-constrained environments, thanks to advancements in training techniques, architecture, and optimization strategies.

Applications of Small Language Models

The versatility of SLMs is one of their most compelling features, finding applications in various domains, including:

1. Sentiment analysis
2. Text summarization
3. Question-answering
4. Code generation

The compact size and efficient computation of SLMs make them well-suited for deployment on edge devices, mobile applications, and resource-constrained environments. For example, Google’s Gemini Nano, featured on the latest Google Pixel phones, assists with text replies and summarizes recordings without requiring an internet connection. Microsoft’s Orca-2–7b and Orca-2–13b are other examples of SLMs.

SLMs vs. LLMs: Key Differences

• Specialization: While LLMs are trained on vast amounts of general data, SLMs excel in specialization through fine-tuning, achieving high accuracy and performance in narrow contexts.
• Computational efficiency: SLMs require significantly less computational power and energy consumption compared to LLMs.
• Inference speed and latency: The compact size of SLMs enables faster processing times, making them more responsive and suitable for real-time applications.
• Cost-effectiveness: The development and deployment of SLMs are often more cost-effective than LLMs, making them an attractive option for smaller organizations and research groups with limited budgets.

Notable Small Language Models

1. Llama 2 (Meta AI)
2. Mistral and Mixtral (Mistral AI)
3. Phi and Orca (Microsoft)
4. Alpaca 7B (Stanford)
5. StableLM (Stability AI)

The Future of Small Language Models

As research and development in this area continue to advance, the future of small language models looks promising. Advanced techniques such as distillation, transfer learning, and innovative training strategies are expected to further enhance the capabilities of these models, potentially closing the performance gap with LLMs in various tasks.

Moreover, the accessibility and cost-effectiveness of SLMs make them an attractive option for organizations looking to leverage AI technology without the substantial computational resources and financial investment required for LLMs. The ability to deploy SLMs on-premises also addresses concerns regarding data privacy and compliance.