Are Larger Language Models Always Better? The rise of Small Language Models
While it’s true that larger language models often achieve impressive performance, defining “better” isn’t straightforward when each use case has its unique requirements. The rise of small language models (SLMs) has shown that bigger isn’t always best. In this article, we will dive deep into the world of small language models. Despite their size, they are emerging and showing promising results, even outperforming larger models in certain applications.
What is a Small Language Model?
The term Small Language Model is self-explanatory — it refers to models that are smaller in size compared to their Large Language Model (LLM) counterparts. While LLMs contain hundreds of billions of parameters, SLMs contain millions to a couple of billion parameters. Although they have fewer parameters, SLMs do not compromise on quality. In fact, several additional features make SLMs competitive and even superior in some use cases.
Architecture
In most cases, SLMs share a similar architecture with LLMs. Techniques such as knowledge distillation, pruning, quantization, and architectural optimization are employed to reduce their size without significantly affecting performance.
Convenience
Due to their reduced size, SLMs require less computational power, making them faster for inference. While running an LLM on an average personal computer is challenging, users can easily run an SLM on their own devices. This accessibility allows more individuals and organizations to leverage advanced language models without investing in expensive hardware.
Data Security
Using commercial LLMs like ChatGPT can pose security concerns for certain tasks, especially when sensitive data is involved. In such cases, open-source SLMs offer a viable solution. A domain-specific open-source SLM can be installed directly on the user’s device, allowing them to benefit from language model capabilities without sharing any information over the web. For example, models like Mistral 7B or Phi-2 can run on average GPUs, and some have quantized versions that can even run on CPUs. Python package Like LaamaCPP can be utilized to run qunatized models on CPU.
Fine-Tuning and Domain-Specific Knowledge
LLMs are trained on billion or even trillion of data points, providing them with a broad understanding of language and general knowledge. However, SLMs can be fine-tuned with small amounts of high-quality data for domain-specific tasks. When depth of knowledge in a specific area is more important than breadth, SLMs excel.
For example, in the healthcare sector, an SLM with domain-specific expertise is preferable to an LLM that has a broad but shallow understanding of various topics. In such cases, quality is preferred over quantity.
Rapid Experimentation and Better Interpretability
SLMs, with their fewer parameters and reduced computational requirements, are well-suited for experimentation. While LLMs require massive computational power and can take months to train or fine-tune, an SLM can be fine-tuned in a week without extensive hardware support. Additionally, because the datasets used are smaller and the domain is well-defined, SLMs are often more interpretable than LLMs. If a newly fine-tuned model does not perform as expected, a new model can be developed and tested quickly.
Environmental Considerations
Since SLMs are not computationally intensive, they can be easily deployed and maintained at low cost. Some SLMs can even be deployed on edge devices like mobile phones. Their lower energy consumption results in a reduced carbon footprint, making them more environmentally friendly.
Conclusion
Small Language Models are at the forefront of democratizing access to advanced language technologies. They demonstrate that through better optimization and architectural design, smaller resources can achieve significant goals. With the promising future of SLMs, we will likely see powerful language models with fewer parameters that are accessible to a wider population.
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