Blogs

Getting big impact with small language models

As powerful, large foundational models continue to dominate conversations around, Small Language Models (SLMs) stand out as the underdog in today’s landscape. Why? SLMs refine capabilities for organisations looking to capture Generative AI without the computational demands of LLMs.  

While larger models such as GPT-4 or BERT are lauded for their broad capabilities, SLMs like Phi-3, Mixtral and Llama 3 offer targeted, agile functionalities. As a result, this has led to a shift towards models tailored for specific applications (healthcare), while emphasising speed, accuracy, and resource efficiency.

What Distinguishes Small Language Models?

Architecture & Scalability:
SLMs use techniques like model distillation and pruning to reduce parameters while maintaining core functionality. This allows businesses to transform an LLM into an SLM. As a result, businesses have more space to focus on specific tasks like text classification or sentiment analysis without losing precision. SLMs are also optimised for low-latency environments, making real-time decision-making and edge applications seamless.  

Training Approaches:
Transfer learning and fine-tuning approaches enable SLMs to absorb knowledge from foundational LLMs and quickly adapt to niche tasks. This is crucial in sectors like healthcare and legal, where general information needs enhancement with domain specifics. By training on smaller, targeted datasets, SLMs achieve high accuracy with minimal data, easing the data labelling burden.

Key Industry Applications for SLMs

Healthcare: Rapid Diagnostics
SLMs are equipped with the ability to interpret unstructured data from electronic health records (EHRs). This enables healthcare industries to attain deep, context-aware insights. An SLM tuned to medical terms and patient interactions can swiftly recommend treatments and detect critical conditions, outpacing general-purpose LLMs. This specialisation is vital in scenarios like emergency rooms where quick decisions are crucial.

Financial Services: Instant Fraud Detection
The finance industry benefits from SLMs’ ability to process real-time transaction data accurately and with low latency. While LLMs offer general insights, an SLM designed for fraud detection can immediately highlight suspicious patterns, stopping fraud before it escalates. SLMs also enhance anti-money laundering processes by identifying industry-specific red flags effectively.

Why should you use SLMs?

  1. Focused Precision
    SLMs specialise in delivering highly accurate results for specific applications. For instance, in legal services where terminology is critical, SLMs outperform generalised models by producing relevant outcomes with fewer false positives.
  1. Efficiency in Resource-Limited Environments
    SLMs are advantageous in settings with limited computational resources. They offer a cost-effective solution for training and deployment compared to LLMs, making AI accessible to small and medium-sized enterprises (SMEs).
  1. Privacy & Security by Design
    Deploying SLMs on-premises or within private clouds significantly reduces data breach risks. This approach is highly valuable in sectors like healthcare and finance, which have strict data privacy regulations.
  1. Real-Time Interactivity
    SLMs excel in applications requiring immediate responses, such as chatbots and IT troubleshooting. Their minimal computational demands ensure instant replies, improving user engagement without costly infrastructure.

The Way Forward: A Hybrid AI Approach

SLMs are designed to act as a complement and not a replacement for LLMs. Essentially, it aims to create a balanced AI ecosystem by enhancing the capabilities of generalist models. The end result is an ideal solution for businesses aiming to balance cost, performance, and specialisation. Moving forward, companies will likely use LLMs for broad tasks and SLMs for detailed precision in day-to-day operations.