This is a guest post for the Computer Weekly Developer Network written by Yuval Illuz, OurCrowd COO & AI and cybersecurity expert.
OurCrowd is a global venture investing platform for institutions and individuals (it vets and selects companies) to invest and engage in emerging companies.
Illuz writes in full as follows…
The world of artificial intelligence is evolving rapidly with constant innovation. There is a need to explore this revolution also by asking whether domain-specific large language models (LLMs) are as effective or potentially superior to less expensive small language models (SLMs).
As businesses and industries increasingly rely on AI to solve challenges, the debate between these two types of models grows louder. While general-purpose LLMs, such as GPT-4 and Gemini, impress with their versatility, domain-specific LLMs zero in on niche sectors like healthcare, finance and cybersecurity. Meanwhile, SLMs offer a leaner and more efficient alternative. Let’s explore the strengths, trade-offs and future potential of these AI powerhouses.
Understanding LLMs & SLMs
To unpack this debate, we first need to understand the players.
LLMs are the heavyweights of AI, trained on massive datasets. This broad exposure equips them to generate human-like text, grasp complicated context and tackle tasks ranging from writing essays to answering technical questions. Their strength is generalisability, an ability to perform well across diverse topics and industries.
On the flip side, SLMs are the nimble, lightweight contenders. Designed with much fewer parameters and less training data, they prioritise efficiency over depth. SLMs shine in resource-constrained environments like edge computing systems such as mobile devices, or on-device applications where speed and low latency trump everything else. While they may not match the contextual skill of LLMs, their practicality makes them vital in certain scenarios.
Enter domain-specific LLMs, a specialised breed of large language models fine-tuned with targeted datasets. Think of them as laser focused LLMs trained on industry-specific information like medical journals, financial compliance requirements, or cybersecurity logs. These models aim to combine the depth of LLMs with a mastery of niche contexts, promising unparalleled precision in their chosen fields. But how do they stack up against SLMs? Let’s break it down.
So let’s compare domain-specific LLMs and SLMs.
#1. Accuracy & relevance
When it comes to accuracy within a specialised field, domain-specific LLMs often lead. Their training on curated, industry-relevant data gives them an edge in understanding jargon, regulations and nuanced patterns that general models might miss. For example, a healthcare LLM trained on decades of medical literature and patient records can assist physicians in diagnosing rare conditions with a precision that rivals human experts. In contrast, an SLM, with its limited scope, might churn out generic responses unfit for such high-stakes scenarios.
In cybersecurity, a domain-specific LLM deployed in a security operations center (SOC) can analyse real-time threat data, identify attack vectors unique to the industry and recommend a mitigation plan faster and more accurately than SLMs. SLMs, while handy for basic text generation or simple queries, lack the depth to handle such specialised demands, making domain-specific LLMs the go-to for industries where precision is non-negotiable.
Real-World Example: Hospital-leveraging healthcare LLMs have reported improved diagnostic accuracy for conditions like rare cancers where symptoms are subtle and data is vast, something SLMs simply can’t replicate without extensive and constant retraining.
#2. Efficiency & resource utilisation
SLMs were born for efficiency, cost saving and performance. With fewer parameters and lower computational demands, they are tailor-made for environments where resources are limited – think mobile apps, IoT devices, or real-time customer service chatbots.
Domain-specific LLMs still require significant horsepower. Fine-tuning these models demands robust hardware, specialised datasets and ongoing maintenance to stay current. For organisations with the budget and infrastructure, this investment pays off in performance. But for smaller firms or applications prioritising speed over specialisation, SLMs remain the practical choice.
Real-World Example: E-commerce giants deploy SLMs for instant chatbot replies, while banks use domain-specific LLMs to crunch financial data and offer tailored services – all is based on the relevant use case.
#3. Adaptability & generalisation
One of domain-specific LLMs’ standout strengths is their adaptability. Industries evolve, for examples, new banking regulations emerge and cybersecurity threats shift. A financial LLM, for instance, can be updated with the latest SEC rules or market trends, ensuring banks and legal firms stay compliant. This flexibility stems from their larger architecture and access to rich, evolving datasets.
SLMs, by contrast, face a challenge. Their compact design limits their ability to absorb and process large-scale updates quickly. Retraining is possible, but their smaller datasets and simpler frameworks make it harder to keep pace with rapid industry shifts. For dynamic fields like finance or healthcare, this can be a dealbreaker.
Real-World Example: After the 2023 SEC cybersecurity disclosure rules, financial LLMs adapted swiftly, helping firms audit compliance documents, an agility SLMs couldn’t match without significant overhaul.
#4 Cost & implementation
Illuz: SLMs shine in resource-constrained environments like edge computing systems.
Building a domain-specific LLM isn’t cheap. The process involves curating high-quality datasets, securing powerful computing resources and employing experts to fine-tune and validate the model. For organizations like pharmaceutical companies, the payoff justifies the expense. But for smaller businesses, it can be too expensive.
SLMs, with their modest resource needs, offer a budget alternative. They’re easier to deploy, requiring less infrastructure and expertise, which makes them a favourite among startups or companies in early AI stage.
Real-World Example: High-end fashion retailers use domain-specific LLMs for AI-driven fashion recommendations, while smaller online stores lean on SLMs for basic support, balancing cost and capability.
A future convergence of strengths
The lines between domain-specific LLMs and SLMs may blur as AI and compute power advances. Techniques like federated learning, where models train collaboratively across devices without centralising data, could empower SLMs with domain-specific smarts, minus the heavy cloud reliance. Meanwhile, optimisation breakthroughs might shrink domain-specific LLMs, making them leaner and more deployable on edge devices (e.g.Nvidia Jetson Orin Nano Super Developer Kit).
In my opinion the hybrid models will emerge quickly, blending SLM efficiency with LLM precision. Such innovations promise a future where businesses of all sizes can harness AI tailored to their needs.
So, are domain-specific LLMs better than SLMs?
It depends on the mission. When accuracy, depth and industry expertise are paramount, domain-specific LLMs shine. But for fast, affordable and resource-light applications like chatbots, SLMs hold their own. As AI evolves, the real winner might be a synergy of both, a kind of hybrid language model (HLM) which would offer scalable, efficient and specialised solutions driving industries forward. For now, the choice hinges on one question: what problem are you solving?
OurCrowd CEO Jon Medved.