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In today’s era of cutthroat competition, no organization can afford to ignore AI. One of the most significant advancements in AI is the rise of language models. If you're unsure whether a Small Language Model (SLM) or a Large Language Model (LLM) is the right fit for your business, this blog is for you. Here, you'll explore the key differences between SLMs and LLMs in terms of their architecture, performance, applications, advantages, and limitations. Based on these, you can easily make an informed decision on selecting the right model for your needs.
What are Large Language Models (LLMs)?
Large Language Models (LLMs) are designed to comprehend user queries and respond in a human-like manner. Built using deep learning techniques, LLMs can process and generate text in a way that closely mimics human language. These models utilize vast datasets and involve a number of parameters to deliver results.
What are Small Language Models (SLMs)?
Small language models are typically based on large language models but of greatly reduced size. They represent specialized subsets within the broader domain of artificial intelligence, tailored for specific queries. They have compact architecture and require less computational power.
Learn more: Opkey’s AI Test Automation
How Language Models Work
Language models work on the concepts of probabilistic machine learning. The key steps in generating natural language are listed below.
General Probabilistic Machine Learning
The objective here is to develop a mathematical model that can predict what comes next with the highest probability. Here, learned probability distribution is used to generate a sequence of phrases based on the available contextual knowledge, i.e. user prompt queries.
Architecture Transformers and Self-Attention
Language models utilize transformer-based deep learning architectures to understand complex relationships between words and sequential phrases. These transformers encode text into numerical representations, assigning weights based on significance to enhance sequence prediction accuracy.
Pretraining and Fine Tuning
Fine-tuning is the process of adapting and updating a pretrained model with new data. Language models are heavily fine-tuned and engineered on specific task domains. The training process includes language model training on domain-specific knowledge, initializing model parameters based on pretrained data, monitoring of model performance, turning model hyperparameters, and training to eliminate bias against unwanted language outcomes such as hate speech and discrimination.
Evaluating the Model Continuously
Both language models involve several qualitative and quantitative assessments such as:
- Perplexity score to measure how well the model predicts a sequence of words
- Blue score to evaluate text generation by comparing model outputs to human-written content
- Human evaluation to assess the model's response for relevance and accuracy
- Bias and fairness testing to identify bias in the model's response
Learn more: Exploring AI in Test Automation
SLM vs. LLM
SLM vs. LLM in Detailed Explanation
Let’s explore a few of the more specific ways large language models differ from small language models, in the following side-by-side comparisons.
Model Size & Complexity
Large Language Models: Larger models are trained on larger and more varied datasets to offer enhanced understanding and performance on complex tasks. The number of parameters included for larger models are billions to trillions.
Small Language Models: Since smaller models are engineered for domain specific tasks, they are trained on smaller and more domain specific datasets. The number of parameters ranges from millions to tens of millions.
Architectural Differences
Large Language Models: Since they consider an immense number of parameters, they demand significant computational power, typically relying on GPUs or TPUs for both training and deployment. Additionally, they require vast and diverse datasets covering multiple domains to ensure effective generalization across various tasks. To capture intricate relationships within data, LLMs leverage deep transformer layers or other sophisticated neural network architectures.
Small Language Models: Unlike LLMs, SLMs have fewer parameters, ranging from a few million to a few hundred million. Due to their compact size, they are more lightweight and require less computational power as compared to LLMs. Their architecture is shallower and more simplified. The training datasets are smaller or more specific and tailored to particular tasks or domains.
Performance and Capabilities
Large Language Models: LLMs can deliver high performance on complex tasks such as nuanced text generation, sophisticated question-answering, and in-depth content creation. Their broad training data and extensive parameterization enable them to handle a diverse range of applications such as translation, summarization, creative writing and coding assistance.
Small Language Models: SLMs are fine-tuned to perform well in specific domains or applications where their training data is highly relevant. They are often trained in industry- or company-specific data sets. Since these models are designed for specific enterprise tasks or domain specific knowledge, they are faster to train and deploy.
Fine-tuning
Large Language Models: The larger models require heavier datasets as compared to SLMs.
Small Language Models: Unlike larger models, smaller models are fine-tuned on a more focused dataset.
Inference speed
Large Language Models: Larger models require multiple parallel processing units to generate data.
Small Language Models: Users can run the model on their local machines and still generate data within acceptable time.
Use-cases
Large language Models: Large models intend to emulate human intelligence at a very broad level and excel in more complex tasks such as content generation or sophisticated data analysis.
Small Language Models: Smaller models are ideal for tasks that require quick responses and lower computational costs, such as basic customer service chatbots or simple data extraction.
For instance, a domain-specific Small Language Model (SLM) can be designed for the healthcare sector and fine-tuned with medical terminologies, procedures, and patient care protocols. Training on medical journals, anonymized patient records, and healthcare-specific literature enables these models to generate highly accurate and contextually relevant outputs.
Which Language Model Is the Right Choice for Your Business?
The answer to this question entirely depends on your use -case and the resources available to you. Large language models are great for general purpose applications. However, if we’re talking specifically about the enterprise domain, broad training on diverse datasets from the internet can lead to gaps in handling industry-specific terminology and nuances, potentially decreasing the effectiveness of their responses.
To excel in a particular domain that requires efficiency and precision, SLMs are considered an ideal choice.
Opkey’s purpose-built ERP SLM, Argus AI, is a prime example of an ERP-Specific Small Language Model (SLM). Trained on a vast repository of business process maps, templates, and test cases within the Opkey core platform, Argus AI leverages years of real-world ERP application experience. Each agent within Argus AI plays a crucial role throughout the ERP lifecycle—from deployment and operation to continuous optimization—helping transform and streamline ERP management.
Learn more: AI-Assisted Test Automation: Revolutionizing Software Testing Like Never Before
In Which Scenario Might a SLM Be a More Appropriate Solution Than an LLM?
Small Language Models can be fine-tuned to perform well on domain specific tasks by training them using your own proprietary data. Smaller models can be engineered to cater to the unique needs of individual enterprises.
Since SLMs are trained on more specialized datasets, they exhibit fewer inaccuracies and a lower risk of producing irrelevant or incorrect information, commonly referred to as “hallucinations.” This enhances the overall relevance and accuracy of their outputs.
Argus AI: Opkey’s ERP-Specific SLM
Opkey is the industry's first platform designed to transform the entire ERP lifecycle: from deployment, to operation, and ultimately – continuous optimization. The core of Opkey platform is a purpose-built ERP SLM – Argus AI, trained leveraging a vast repository of industry-specific process maps, test cases, and templates, designed to simplify and streamline how businesses manage and maintain their ERPs.
Learn more: Opkey’s AI-powered ERP Lifecycle Optimization Platform
Benefits of Small Language Models
Tailored Efficiency and Precision: Small language models are engineered to serve more specific, often niche, purposes within an enterprise.
Cost-Effectiveness: SLMs require less computational power, hence require low financial costs.
Enhanced Security and Privacy: Since SLMs can be deployed on-premises or in private cloud environments, they offer enhanced security and privacy as sensitive information remains within the control of the organization.
Adaptability: They offer a degree of adaptability and responsiveness that is crucial for real-time applications. Their adaptability facilitates easier and quicker updates to model training.
