With the emerging use case of natural language processing (NLP), two main language models come out as leaders - large language models (LLMs) and small language models (SLMs). These 2 models may seem similar but differ in their size (number of parameters), capabilities, and applications.
In this article, we'll highlight some key distinctions between LLMs and SLMs, learning their strengths, limitations, applications, and factors to determine which model is suited for a particular task. First of all, let us understand what language models are.
Language models are AI computing models designed to perform human-like language tasks, after training on a bunch of data. These models are trained as probabilistic machine learning models - finding a probability distribution of words suitable for phrase generation. They are mainly trained on large datasets of text, such as articles or books. Models from this training data then use the pattern to find the next word in a sentence or even generate a new phrase.
What is LLM?
LLM (Large Language Model) is an Artificial Intelligence (AI) model that can analyze and generate text, among other tasks. LLMs are trained on huge datasets hence named "large". LLMs are built on machine learning: specifically, a type of neural network called a transformer model. Many LLMs are trained on data fetched from the internet. However, the quality of the data samples impacts how well large language models learn natural language.
Large Language Models use deep learning (a type of machine learning) to understand how characters, words, numerals, and sentences function together. Deep learning includes the probabilistic analysis of unstructured data, which enables deep learning models to understand distinctions between parts of content without human interference.
LLMs are further fine-tuned or prompt-tuned to perform a particular task, such as interpreting questions, generating responses, or translating text.
What is SLM?
SLM (Small Language Model) are small versions of their LLM counterparts. They have significantly fewer parameters, ranging from a few million to a few billion. The word "small" in the model suggests the smaller amount of data that SLMs are trained on, and the smaller neural network architecture.
Small Language Models are built using statistical methods and small neural networks make them more efficient but less powerful when handling complex tasks. These models are tailored for the specific use case, where they can perform their larger counterparts because of their focused training on specific tasks or domains.
SLMs are more cost-effective and require fewer resources, making them accessible for small organizations with limited budgets and infrastructure. However, SLMs do not match the accuracy of LLMs, they are highly efficient in applications that demand specialized performance and instant deployment.
Read More: How does the Small Language Model work?
LLMs vs SLMs
For training, both LLM and SLM follow similar concepts of probabilistic machine learning for designing architecture, generating responses, and evaluating models.
Several key differences can be considered while comparing two language models - LLM vs SLM. These differences arise from their architecture, data training, capabilities, and specific use cases.
Size and model complexity
Size and complexity is one of the key differences between LLMs and SLMs. SLM typically has a few parameters ranging from a few million to a few billion. In contrast, SLMs have around 7 billion parameters, a small fraction of their LLM counterparts, having parameters ranging up to a trillion. Open source SLM like Mistral 7B contains 7 billion parameters.
Having a larger number of parameters in the LLM model allows them to process and understand more complex relationships and nuances with natural language.
The difference in size comes down to the training process in the model architecture. SLMs use simpler statistical methods or smaller neural network architectures whereas, LLMs use deep learning architectures such as transformers with multiple layers and attention mechanisms.
Training data
SLMs are trained on smaller and more focused datasets, whereas LLMs are trained on huge datasets that range several domains and include billions of characters. SLMs are limited to a specific type of text or domain, constraining generalization, whereas LLMs often include multilingual and multimode data, enhancing context understanding and generalization.
Contextual understanding and domain-specificity
Since SLMs are trained on domain-specific data they may lack holistic contextual information from several knowledge domains but are likely to be efficient in their chosen domain. On the other hand, LLM tends to imitate human intelligence on a wider level. Trained on a larger dataset, LLM is expected to perform relatively well on all domains compared to a domain-specific SLM.
This signifies LLMs are more versatile and adaptive, improved and customized for better downstream tasks like programming.
Resource consumption
LLMs demand significant memory and computational resources, whereas SLMs are economical when it comes to resource consumption. Training an LLM requires GPU compute resources at scale in the cloud. LLM models like ChatGPT require thousands of GPUs for training, on the other hand, the SLM model Mistral 7B is capable of running on a local machine with a decent GPU. However, training Mistral 7B still requires some computing hours across multiple GPUs.
Bias
LLMS tends to be biased. This is because the LLM is not adequately fine-tuned and is being trained on data available on the internet. Because of the training data source, the training data may likely - be labeled incorrectly or misrepresents certain group of content.
Further complexity arises elsewhere: language itself introduces its own bias, depending on several factors like geographic location, dialect, and grammar rules.
Since the SLM trains on smaller and domain-specific data sources, the risk of bias lowers when compared to LLMs.
Inference speed
SLM having a smaller model size lets users run the model on their local system and generate responses. Compared to LLM, which requires several parallel processing units to generate a response. LLM inference tends to slow down depending on the number of users accessing the model.
Performance and capabilities
SLM tends to be less accurate and less sophisticated in understanding the context than LLM, which has high accuracy in understanding natural language and generation due to extensive data training.
SLM is limited to short-range dependencies, often struggling while maintaining context over long given text. Whereas, LLM is capable of maintaining context over long text, generating relevant responses.
SLM is best suited for narrow, domain-specific tasks including simple auto-complete or chatbots. On the other hand, LLMs are capable of performing various tasks such as summarization, translation, and complex questioning-answering.
Applications of SLMs
Text Generation and Summarization: SLMs are capable of generating coherent relevant text, making them suitable for applications like text summarization, paraphrasing, and content generation.
Sentiment Analysis: In the age of social media and online reviews, sentiment analysis is crucial which helps in understanding people's opinions. Thus, SLM aids in sentiment analysis by understanding text to classify the tone (positive, negative, or neutral) of text, social media posts, or customer feedback.
Spam and Fraud Detection: Detect and filter out spam or junk emails based on content patterns. Analyze transactions and activities to identify potential fraud in cybersecurity and finance.
Text Clarification and Information Retrieval: SLMs excel in tasks like document classification, topic modeling, and retrieving information, guiding in organizing and retrieving large amounts of data efficiently.
Language Translation: With advancements in machine translation techniques, SLMs are capable of improving the efficiency of language translation systems. Their smaller size allows faster language translation while accuracy.
Personalized Recommendations: Many e-commerce platforms use SLMs to understand visitors' behavior for personalized product recommendations. By analyzing visitor's intent, SLMs improve the overall shopping experience and bring engagement.
Applications of LLMs
Content Creation: LLMs let users generate content from articles to blogs to short summaries, stories, surveys, questionnaires, and social media posts. The quality and accuracy depend on the information given in the input prompt.
Virtual Assistants for Customer Support: LLMs are the core of AI-powered virtual assistants/chatbots that understand and process natural language. When a user asks a question, the LLM interprets the intent of the request. Once LLM understands the intent it generates a most relevant response.
Code Generation: LLMs can assist programmers write, review, and debug codes in Python, JavaScript, Java, C#, and PHP languages. This model understands and generates code snippets, and even writes functions based on given descriptions. Also, LLM can translate code from one language to another, making it easier for programmers to work with unfamiliar syntax.
Question and Answering: Question answering is a widespread application of LLM. LLMs easily understand and generate human-like texts, making them suitable for generating accurate and contextually relevant answers to various questions.
Sales Automation: The LLM model performs several segments of the sales cycle from lead generation to nurturing, enabling the sales team to automate their tasks. LLM can analyze data and find potential prospects while understanding their preferences and curating personalized recommendations.
LLM vs SLM - Which model is right for you?
Choosing the right language model depends on the use case, what task you want to perform, and the resources available. Since LLM offers unmatched accuracy, making it suitable for organizations to use it as a chat agent for their sales and customer support team. But, for a domain-specific use case, SLM is likely to excel.
Businesses can achieve a balanced approach, that meets their need by implementing LLMs for prototyping and eventually optimizing with SLMs.
Consider applications of language models in finance, legal, and medical. Each application requires specialized industry knowledge. Training and fine-tuning LLMs with internal knowledge can serve as virtual assistants for several use cases in specialized industries.
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