Choosing the right language model to integrate generative AI can feel overwhelming, especially given the rapid proliferation of options, like LLM, multimodal LLM, and SLM. For instance, you might want to enhance customer service with a conversational AI tool. Upon researching, you find that these models come with their own sets of strengths and weaknesses. With so many choices available, how do you know which one is right for your specific use case? Analyzing the differences between LLMs and SLMs can provide significant insights to help you make an informed decision.
LamaTech's Generative AI Tech Stack simplifies this task by enabling you to find the ideal model for your needs and objectives quickly.
What is a Large Language Model (LLM)?
Large language models (LLMs) are a category of foundation models trained on immense amounts of data. They are capable of understanding and generating natural language and other types of content to perform a wide range of tasks.LLMs have become household names thanks to their role in bringing generative AI to the forefront of public interest and the focus organizations are focusing on adopting artificial intelligence across numerous business functions and use cases.
Evolution of LLMs and Generative AI
Outside of the enterprise context, LLMs and new developments in generative AI have arrived out of the blue. Many companies, including IBM, have spent years implementing LLMs at different levels to enhance their natural language understanding (NLU) and natural language processing (NLP) capabilities. This has occurred alongside advances in:
- Machine learning
- Machine learning models
- Algorithms
- Neural networks
- Transformer models
Foundation Models Like LLMs
LLMs are a class of foundation models, which are trained on enormous amounts of data to provide the foundational capabilities needed to drive multiple use cases and applications and resolve many tasks. This starkly contrasts the idea of building and training domain-specific models for each of these use cases individually, which is prohibitive under many criteria (most importantly cost and infrastructure), stifles synergies and can even lead to inferior performance.
Examples of Leading Large Language Models
LLMs represent a significant breakthrough in NLP and artificial intelligence and are easily accessible to the public through interfaces like Open AI’s Chat GPT-3 and GPT-4, which have garnered the support of Microsoft. Other examples include:
- Meta’s Llama models
- Google’s bidirectional encoder representations from transformers (BERT/RoBERTa)
- PaLM models
IBM also recently launched its Granite model series on watsonx.ai, which has become the generative AI backbone for other IBM products like Watson Assistant and Watson Orchestrate.
Capabilities and Applications of LLMs
LLMs are designed to understand and generate text like a human, in addition to other forms of content, based on the vast amount of data used to train them. They can:
- Infer from context
- Generate coherent and contextually relevant responses
- Translate to languages other than English
- Summarize text
- Answer questions (general conversation and FAQs)
- Assist in creative writing or code generation tasks.
They can do this thanks to billions of parameters that enable them to capture intricate patterns in language and perform a wide array of language-related tasks. LLMs are revolutionizing applications in various fields, from chatbots and virtual assistants to content generation, research assistance and language translation.As they continue to evolve and improve, LLMs are poised to reshape how we interact with technology and access information, making them a pivotal part of the modern digital landscape.
How LLMs Work: The Nuts and Bolts of AI Text Generation
LLMs operate by leveraging deep learning techniques and vast amounts of textual data. These models are typically based on a transformer architecture, like the generative pre-trained transformer, which excels at handling sequential data like text input.
The Architecture of LLMs
LLMs consist of multiple layers of neural networks, each with parameters that can be fine-tuned during training. These layers are enhanced further by a multitude of layers known as the attention mechanism, which dials in on specific parts of data sets.
The Training Process of LLMs
During the training process, these models learn to predict the next word in a sentence based on the context provided by the preceding words. The model does this through attributing a probability score to the recurrence of words that have been tokenized, broken down into smaller sequences of characters. These tokens are then transformed into embeddings, which are numeric representations of this context.
The Power of LLMs in Language Generation
To ensure accuracy, this process involves training the LLM on a massive corpora of text (billions of pages), allowing it to learn grammar, semantics, and conceptual relationships through zero-shot and self-supervised learning.
Once trained on this training data, LLMs can generate text by autonomously predicting the next word based on the input they receive and drawing on the patterns and knowledge they've acquired. The result is coherent and contextually relevant language generation that can be harnessed for a wide range of NLU and content generation tasks.
Mitigating Bias and Hallucinations in LLMs
Model performance can also be increased through prompt engineering, prompt-tuning, fine-tuning and other tactics like reinforcement learning with human feedback (RLHF) to remove the biases, hateful speech and factually incorrect answers known as “hallucinations” that are often unwanted byproducts of training on so much unstructured data.
This is one of the most important aspects of ensuring enterprise-grade LLMs are ready for use and do not expose organizations to unwanted liability, or cause damage to their reputation.
LLMs: The Future of Business Process Automation
LLMs are redefining an increasing number of business processes and have proven their versatility across various use cases and tasks in various industries. They augment conversational AI in chatbots and virtual assistants (like IBM watsonx Assistant and Google’s BARD) to enhance the interactions that underpin excellence in customer care, providing context-aware responses that mimic interactions with human agents.
Content Creation and Knowledge Discovery
LLMs also excel in content generation, automating content creation for blog articles, marketing or sales materials and other writing tasks. In research and academia, they aid in summarizing and extracting information from vast datasets, accelerating knowledge discovery. LLMs also play a vital role in language translation, breaking down language barriers by providing accurate and contextually relevant translations. They can even be used to write code, or “translate” between programming languages.
Accessibility and Industry Transformation
They contribute to accessibility by assisting individuals with disabilities, including through text-to-speech applications and generating content in accessible formats. From healthcare to finance, LLMs are transforming industries by streamlining processes, improving customer experiences, and enabling more efficient and data-driven decision-making. Most excitingly, all of these capabilities are easy to access, in some cases literally an API integration away. Here is a list of some of the most important areas where LLMs benefit organizations:
- Text generation: language generation abilities, such as writing emails, blog posts or other mid-to-long form content in response to prompts that can be refined and polished. An excellent example is retrieval-augmented generation (RAG).
- Content summarization: summarize long articles, news stories, research reports, corporate documentation and even customer history into thorough texts tailored in length to the output format.
- AI assistants: chatbots that answer customer queries, perform backend tasks and provide detailed information in natural language as a part of an integrated, self-serve customer care solution.
- Code generation: assists developers in building applications, finding errors in code and uncovering security issues in multiple programming languages, even “translating” between them.
- Sentiment analysis: analyze text to determine the customer’s tone in order understand customer feedback at scale and aid in brand reputation management.
- Language translation: provides wider coverage to organizations across languages and geographies with fluent translations and multilingual capabilities.
LLMs stand to impact every industry, from finance to insurance, human resources to healthcare and beyond, by automating customer self-service, accelerating response times on an increasing number of tasks as well as providing greater accuracy, enhanced routing and intelligent context gathering.
Related Reading
- LLM Security Risks
- What is an LLM Agent
- AI in Retail
- LLM Deployment
- How to Run LLM Locally
- How to Use LLM
- LLM Model Comparison
- AI-Powered Personalization
- How to Train Your Own LLM
What is a Small Language Model (SLM)?
A small language model is a machine learning model typically based on a large language model but of greatly reduced size. It retains much of the functionality of the large language model from which it is built but with far less complexity and computing resource demand.
How are SLMs Used?
Small language models can do most of what large language models can do. They can provide conversational responses to text, draw on a training data set to return answers to queries, generate images, or even analyze visual (computer vision) and audio data inputs. Small language models are still emerging but show great promise for very focused AI use cases.
For example, an SLM might be an excellent tool for building an internal documentation chatbot trained to provide employees with references to an org’s resources when asking common questions or using certain keywords. While an SLM may not be able to draw upon the vast training data sets of an LLM, a properly tuned SLM could still retain much of the natural, conversational experience of such a model — just with a much-narrowed set of data (and, notably, marginally reduced accuracy).
In a computer vision scenario, you might train an SLM to identify all objects that contain a particular type (just fruit, for example), versus labeling every known object from a massive training data set:
- Foods
- Animals
- Vehicles
- People
- Plants
- Signs
- Products
Just How “Small” is a Small Language Model?
Small language models vary greatly in size. All language models tend to be measured in terms of the number of parameters inside the model, as these parameters govern the size (and inherent complexity — and thus computing demand) of a given model.
The Scale of Language Models
Cutting-edge large language models like OpenAI’s GPT-4 and GPT-4o are estimated to have over 1 trillion parameters (OpenAI does not publish official parameter counts for its models). Microsoft’s latest small language model, Phi-3, uses as few as 3.8 billion parameters, and up to 14 billion. That makes Phi-3 between 0.38% and 1.4% the size of GPT-4o. Some very small language models have parameters measured in the tens of millions.
Hardware Requirements of Large Language Models
The size of language models is particularly relevant because these models run in memory on a computer system. This means it’s not so much about physical disk space as it is the dedicated memory to run a model. A model like GPT-4o requires a large cluster of dedicated data center AI servers running expensive specialty hardware from a vendor like NVIDIA to run at all — it’s estimated that OpenAI’s model needs many hundreds of gigabytes of available memory.
Trade-off Between Model Size and Hardware Requirements
There is no realistic way to make such a model run even on a powerful desktop computer. A small language model, by comparison, might require just a few gigabytes of memory (RAM), meaning that even a high-end smartphone would be capable of running such a model, given it contained dedicated AI coprocessing hardware (aka an NPU) to run at a reasonable speed.
How Will SLMs Be Used in the Future?
The future of small language models seems likely to manifest in end device use cases — on laptops, smartphones, desktop computers, and perhaps even kiosks or other embedded systems. Imagine a check-in kiosk at a doctor’s office that can use a camera to read your insurance or ID card, ask you questions about the reason for your visit with voice input, and provide you with answers to questions about the facility (where’s the bathroom, how long is the wait typically, what’s my doctor’s name).
Think about shopping at a big box store, walking up to an automated stock-checking robot, asking where the coconut milk is, and instantly getting a reply with in-store directions shown on display. In an enterprise setting, an SLM could be connected to a corporate knowledge base and organizational chart, connecting the dots between projects and stakeholders that typically require tedious outreach and repetitive question asking and answering. This SLM could run directly inside the corporate chat service on your smartphone.
Related Reading
- How to Fine Tune LLM
- How to Build Your Own LLM
- LLM Function Calling
- LLM Prompting
- What LLM Does Copilot Use
- LLM Evaluation Metrics
- LLM Use Cases
- LLM Sentiment Analysis
- LLM Evaluation Framework
- LLM Benchmarks
- Best LLM for Coding
Detailed LLM vs SLM Comparison
Large language models (LLMs) and small language models (SLMs) are both types of artificial intelligence (AI) systems that are trained to interpret human language, including programming languages. The key differences between them are usually the size of the data sets they’re trained on, the different processes used to train them on those data sets, and the cost/benefit of getting started for different use cases.
LLMs and SLMs: Language Data
As their names suggest, both LLMs and SLMs are trained on data sets consisting of language, distinguishing them from models trained on images (e.g., DALL·E) or videos (e.g., Sora). Examples of language-based data sets include webpage text, developer code, emails, and manuals.
Generative and Non-Generative Applications
One of the most well-known applications of both SLMs and LLMs is generative AI (gen AI), which can generate—hence the name—unscripted content responses to many different, unpredictable queries. LLMs in particular have become well known among the general public thanks to the GPT-4 foundation model and ChatGPT, a conversational chatbot trained on massive data sets using trillions of parameters to respond to a wide range of human queries.
Though gen AI is popular, there are also non-generative applications of LLMs and SLMs too like predictive AI.
Data Set Differences
LLMs and SLMs are usually trained on different data sets. The scope of GPT-4/ChatGPT is an excellent example that demonstrates one common difference between them: the data sets they’re trained on.
Broad Scope of LLMs
LLMs are usually intended to emulate human intelligence at a very broad level, and thus are trained on a wide range of large data sets. In the case of GPT-4/ChatGPT, that includes the entire public internet up to a certain date. This is how ChatGPT has gained notoriety for interpreting and responding to such a wide range of queries from general users. This is also why it has sometimes gained attention for potentially incorrect responses, colloquially referred to as “hallucinations”—it lacks the fine-tuning and domain-specific training to respond to every industry-specific or niche query accurately.
Domain-Specific Focus of SLMs
SLMs on the other hand are typically trained on smaller data sets tailored to specific industry domains (i.e. areas of expertise). For example, a healthcare provider could use an SLM-powered chatbot trained on medical data sets to inject domain-specific knowledge into a user’s non-expert query about their health, enriching the quality of the question and response. In this case, the SLM-powered chatbot doesn’t need to be trained on the entire internet—every blog post or fictional novel or poem ever written—because it’s irrelevant to the healthcare use case. In short, SLMs typically excel in specific domains but struggle compared to LLMs in general knowledge and overall contextual understanding.
Training Differences: LLMs Require More Resources to Train and Fine-Tune
SLMs and LLMs have different training processes. The size and scope of data sets isn’t the only factor in differentiating SLMs from LLMs, and importantly, a model can be considered an SLM even if it’s trained on the same data sets as an LLM.
That’s because the training parameters and overall process—not just the amount of data—are part of defining each model. In other words, what’s important isn’t just how much data a model is trained on, but also what it is designed to learn from that data.
Parameters
In machine learning, parameters are internal variables that determine what predictions a model will make. In other words, parameters are how models decide what to do with the raw material of the data set. During training, an AI model continuously adjusts its parameters to improve predictions—think of it like turning a knob on a radio to find the right channel. Beyond the total number of parameters, other factors in this immensely complicated process include:
- How parameters are layered into a model
- How they’re weighted against each other
- How they’re optimized for pattern recognition versus simple memorization
There’s no clear industry definition for how many parameters equate to an SLM versus an LLM. Instead, what’s most relevant is that SLMs typically contain far fewer parameters than LLMs because their use cases are more focused on specific knowledge domains. In the case of the LLM GPT-4/ChatGPT, it was purportedly trained on trillions of parameters to respond to almost any user input. It’s worth noting that GPT-4 is a uniquely large example of an LLM.
There are many examples of smaller LLMs (not quite SLMs), like IBM’s open-source Granite models, which range in size from 3 to 35 billion parameters. SLMs typically boast fewer parameters (sometimes still in the billions) because the expected applications are much narrower.
Fine-Tuning
Fine-tuning, another aspect of model training that can differentiate SLMs and LLMs, is adapting and updating a pre-trained model with new data. It typically involves customizing a pre-trained model to a specific use case. This involves introducing new data sets to test whether the existing parameters can still produce acceptable results in a new context. In general, fine-tuning is harder, takes more time, and is more resource-intensive the more parameters a model contains, meaning LLMs require a heavier lift than SLMs. Beyond parameters and fine-tuning, the type and complexity of the training process are also usually different between SLMs and LLMs. Understanding different types of model training, like “self-attention mechanisms” or “encoder-decoder model schemes,” requires high data science expertise. The basic differences between SLM and LLM training are that SLMs usually favor more resource-efficient approaches and focus on specific use cases than their LLM counterparts.
Bias
Although every AI model undergoes some degree of fine-tuning, the scope of most LLMs needs to be revised to tune them to every possible inference. LLMs are also typically trained on openly accessible data sets like the Internet, whereas SLMs often train on industry—or company-specific data sets. This can introduce biases, such as the underrepresentation or misrepresentation of certain groups and ideas or factual inaccuracies. Because LLMs and SLMs are language models, they can also inherit language biases related to dialect, geographical location, and grammar. In short, any language model can inherit bias, but LLMs, in particular, given their scope, introduce more opportunities for bias. With SLMs trained on smaller data sets, you can more easily mitigate the biases that will inevitably occur.
Resource Requirements: Training LLMs Is Resource Intensive
LLMs and SLMs require different resources. Training any model for a business use case, whether LLM or SLM, is a resource-intensive process. However, training LLMs is especially resource intensive. In the case of GPT-4, a total of 25,000 NVIDIA A100 GPUs ran simultaneously and continuously for 90-100 days. Again, GPT-4 represents the largest end of the LLM spectrum. Other LLMs like Granite didn’t require as many resources. Training an SLM still likely requires significant compute resources, but far fewer than an LLM requires.
Resource Requirements for Training vs. Inference
It’s also important to note the difference between model training and model inference. As discussed above, training is the first step in developing an AI model. Inference is the process a trained AI model follows to predict new data. For example, when a user asks ChatGPT a question, ChatGPT invokes to return a prediction to the user—that process of generating a prediction is an inference. Some pretrained LLMs, like the Granite family of models, can make inferences using the resources of a single high-power workstation (e.g., Granite models can fit on one V100-32GB GPU2). Many require multiple parallel processing units to generate data. Furthermore, the more concurrent users accessing an LLM, the slower the model runs inferences. SLMs, on the other hand, are usually designed to make inferences with a smartphone or other mobile device's resources.
Cost and Benefits of Getting Started with LLMs vs. SLMs
There’s no answer to the question, “which model is better?” Instead, it depends on your organization’s plans, resources, expertise, timetable, and other factors. It’s also important to decide whether your use case necessitates training a model from scratch or fine-tuning a pretrained model. Common considerations between LLMs and SLMs include:
Cost
LLMs generally require far more resources to train, fine-tune, and run inferences. Importantly, training is a less frequent investment. Computing resources are only needed while a model is being trained, which is an intermittent and not continuous task. Running inferences represents an ongoing cost, and the need can increase as the model is scaled to more users. In most cases, this requires large-scale cloud computing resources, a significant on-premise resource investment, or both. SLMs are frequently evaluated for low-latency use cases, like edge computing. That’s because they can often run with just the resources on a single mobile device without needing a constant, strong connection to more significant resources.
Expertise
Many popular pre-trained LLMs―like Granite, Llama, and GPT-4― offer a more “plug-and-play” option for getting started with AI. These are often preferable for organizations looking to begin experimenting with AI since they don’t need to be designed and trained from scratch by data scientists. SLMs, on the other hand, typically require specialized expertise in both data science and industry knowledge domains to accurately fine-tune on niche data sets.
Security
One potential risk of LLMs is the exposure of sensitive data through application programming interfaces (APIs). Specifically, fine-tuning an LLM on your organization’s data requires careful attention to compliance and company policy. SLMs may present a lower risk of data leakage because they offer greater control.
When to Use LLMs or SLMs
Utilize Language Models (LLMs) for tasks demanding a profound grasp of natural language and the creation of text resembling human expression. These tasks encompass language translation, text summarization, and content generation. LLMs are also highly beneficial for tasks that require answering open-ended questions and for conversational agents, as they can produce contextually relevant and coherent responses. SLMs are ideal for tasks that demand a more structured comprehension of language, like sentiment analysis, named entity recognition, and text classification. Their usage enables precise understanding and effective processing of textual data.
SLMs are highly effective in scenarios where the primary objective is to extract specific information or identify patterns within the text. Their application proves invaluable when precision and discernment are paramount. SLMs prove invaluable in tasks that require deciphering the connections among various textual elements, such as discerning a sentence's sentiment or categorizing a document's subject matter. To effectively decide between an LLM or an SLM for a given NLP task, it is crucial to comprehend the specific requirements. Each model type has unique strengths and limitations, making this understanding essential.
Choosing the Right Model
The best choice depends on what you specifically need and the context you’re in. Consider the following factors when choosing between an SLM and an LLM:
- Resource constraints: An SLM is the obvious choice if you have limited computational power or memory.
- Task complexity: An LLM might be necessary for highly complex tasks to ensure optimal performance.
- Domain specificity: If your task is specific to a particular domain, you can fine-tune either model on relevant data. However, SLMs may hold an advantage in this regard.
- Interpretability: If understanding the model’s reasoning is vital, an SLM would be preferred.
Fine-Tuning Capabilities
Fine-tuning in machine learning refers to training a pre-existing, often expansive, versatile model on a specific task or dataset. This enables the model to adapt its acquired knowledge to a particular domain or set of tasks. The concept behind fine-tuning is to harness the insights gained by the model during its initial training on a vast and varied dataset and subsequently tailor it for a more focused and specialized application.
Fine-Tuning of LLMs
LLMs like GPT-3 or BERT can be fine-tuned using task-specific data, enhancing their ability to generate precise and relevant text in context. This approach is crucial because training a large language model from scratch is extremely costly in terms of computational resources and time. By leveraging the knowledge already captured in pre-trained models, we can achieve high performance on specific tasks with significantly less data and computing. Fine-tuning plays a vital role in machine learning when we need to adapt an existing model to a specific task or domain.
Here are some important moments that require your attention. Make sure not to overlook these key scenarios:
- Transfer Learning: Fine-tuning plays a critical role in transfer learning, allowing the knowledge of a pre-trained model to be applied to a new task. The training process is expedited by starting with a pre-trained model and refining it for a specific task. The model can effectively leverage its general language understanding for the new task. This approach saves time and enables the model to harness its expertise in delivering high-quality, customized software solutions.
- Limited Data Availability: Fine-tuning proves especially advantageous when working with limited labeled data for a specific task. Rather than starting from scratch, you can harness the knowledge of a pre-trained model and adapt it to your task using a smaller dataset.
Fine-Tuning of SLMs
SLMs can also be fine-tuned to enhance their performance. Fine-tuning involves exposing an SLM to specialized training data and tailoring its capabilities to a specific domain or task. Similar to sharpening a skill, this process enhances the SLM’s ability to produce accurate, relevant, and high-quality outputs.
Recent studies have demonstrated that smaller language models can be fine-tuned to achieve competitive or even superior performance compared to their larger counterparts in specific tasks. This makes SLMs a cost-effective and efficient choice for many applications. Thus, we can agree that both LLMs and SLMs have robust fine-tuning capabilities that allow them to be tailored to specific tasks or domains, enhancing their performance and utility in various applications.
Unleashing Potential of LLMs & SLMs Across Industries
E-commerce Platform Use Case: Customer Support Chatbot
In this scenario, an e-commerce platform leverages an LLM to empower a customer support chatbot. The LLM is trained to comprehend and generate human-like responses to customer inquiries. This enables the chatbot to deliver personalized and contextually relevant assistance, including addressing product-related queries, order tracking, and general inquiries.
The LLM's deep language understanding and contextual relevance elevate the customer support experience, leading to enhanced satisfaction and operational efficiency.
SLMs in Action Industry: Financial Services Firm Use Case: Sentiment Analysis for Customer Feedback
In this case, a financial services firm utilizes an SLM for sentiment analysis of customer feedback. The SLM is trained to categorize customer reviews, emails, and social media comments into positive, negative, or neutral sentiments.
Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack that empowers teams to rapidly implement AI solutions without accruing tech debt. Our solution addresses the core challenges of implementing Generative AI by providing:
- Managed middleware
- Custom API (GraphQL)
- Automated workflow (CI/CD)
- DevOps for GenAI
- Integrated vector database (Weaviate)
These tools make it easier for teams to get to production quickly, automating workflows and ensuring production-grade deployment on the edge. Start building GenAI apps for free today with Lamatic’s managed GenAI solution.
What Does Lamatic’s Middleware Do?
Lamatic’s managed Generative AI middleware streamlines the implementation of AI solutions to accelerate development timelines and reduce technical debt.
- It helps teams avoid starting from scratch by offering a flexible structure on which to build their solutions.
- This allows developers to focus on customizing their applications instead of worrying about how the different parts of their solutions will communicate.
- Our middleware also helps automate workflows to minimize manual processes for faster, more efficient deployments.
How Do Lamatic’s Custom APIs Accelerate GenAI Development?
Lamatic’s custom application programming interfaces (APIs) accelerate Generative AI development by providing a clear pathway for applications to communicate with AI models. Our GraphQL API makes it easy for developers to implement Generative AI capabilities in their products by allowing for more efficient data queries. Less data is transferred between the application and AI model, enabling faster response times and smoother user experiences.
What Is Lamatic’s Low-Code Agent Builder?
Lamatic’s low-code agent builder allows developers to create customization agents that help tailor Generative AI outputs to meet specific user and business needs. The Builder provides a user-friendly interface that simplifies the agent-building process so that developers can create these customization agents quickly without extensive training or knowledge of AI.
