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What are SLMs and LAMs?

AI & Automation
Digital Transformation
GenAI
What are SLMs and LAMs?
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What are SLMs and LAMs?

AI & Automation
Digital Transformation
GenAI
What are SLMs and LAMs?
AI & Automation

As AI tech evolves, new terminologies and concepts are emerging. Small Language Models (SLMs) and large action models (LAMs) are gaining traction and will soon become widely accepted.  

This blog explains these two intriguing AI terms and their potential impact on various use cases, especially if you plan to implement them.

Understanding Small Language Models (SLMs)

Small Language Models, or SLMs, are designed to handle specific scenarios or functionalities. Unlike large language models (LLMs), which are trained on extensive datasets covering a wide range of scenarios, SLMs focus on niche areas. Both SLMs and LLMs are probabilistic models, but the number of parameters they contain differs significantly. Typically, LLMs have 500 to 1000 times more parameters than SLMs.

So, why opt for SLMs? The primary advantage of SLMs lies in their specificity and efficiency. By concentrating on a particular use case, SLMs can offer greater accuracy and specialization. For instance, consider a use case involving medical pathology terms. An SLM tailored to this domain would likely perform better than a general-purpose LLM. Additionally, SLMs tend to provide faster responses and require less processing power, making them more resource efficient.

Real-World Applications of SLMs

Medical Diagnostics: Imagine a healthcare setting where a diagnostic tool needs to interpret specific medical tests and pathology reports. An SLM trained exclusively in medical data can provide highly accurate diagnoses and suggest treatment plans more efficiently than a general LLM. This focused approach ensures that the model understands the nuances of medical terminology and procedures, leading to better patient outcomes.

Legal Document Analysis: Analysing contracts and legal documents requires a deep understanding of legal jargon and context. An SLM trained in legal texts can quickly review documents, identify key clauses, and suggest modifications, thereby saving lawyers significant time and reducing the risk of missing critical information.

Customer Service in Niche Industries: Consider a customer service chatbot for a highly specialized industry, such as aviation. An SLM trained in aviation-specific terminology and scenarios can provide accurate and relevant responses to customer inquiries, improving the overall customer experience and reducing the workload on human agents.

Introducing Large Action Models (LAMs)

Large Action Models, or LAMs, represent a step beyond LLMs, particularly in terms of complex multi-step reasoning and decision-making capabilities. LAMs are trained on datasets that emphasize strategic thinking, decision-making, and the ability to autonomously execute multi-layered and interconnected tasks. This advanced level of functionality brings LAMs closer to the realm of Artificial General Intelligence (AGI).

LAMs are designed to optimize tasks that require strategic planning and autonomous action-taking. Their ability to handle intricate and interconnected tasks makes them ideal for scenarios where complex decision-making is crucial.

Real-World Applications of LAMs

Autonomous Vehicles: Imagine an autonomous vehicle navigating a busy city. A LAM can process vast amounts of data from various sensors, make real-time decisions about route optimization, traffic conditions, and potential hazards, and execute driving actions autonomously. This capability ensures a safe and efficient journey.

Supply Chain Management: In the context of supply chain management, a LAM can oversee the entire supply chain, making strategic decisions about inventory levels, supplier selection, and logistics. It can autonomously handle disruptions, such as delays or shortages, by re-routing shipments and adjusting orders, ensuring smooth operations.

Disaster Response: Rapid and strategic decision-making is crucial during natural disasters. A LAM can coordinate rescue operations, allocate resources, and optimize routes for emergency vehicles. Its ability to process real-time data and make complex decisions can significantly enhance the efficiency of disaster response efforts.

Example: You are commandeering a spaceship with AI

Let's delve into a hypothetical scenario to better understand the concepts of SLMs and LAMs. Imagine you are in a futuristic movie set on a spaceship heading toward Alpha Centauri to establish a new human base. The journey will take ten years, and all human crew members, including you, are in stasis. Who would you entrust with navigating and making decisions on the spaceship while you are in stasis – a LAM, LLM, or SLM? The correct choice here would be a LAM. Given its advanced strategic thinking and autonomous decision-making capabilities, a LAM would be best suited to handle the complex tasks involved in navigating the spaceship and ensuring its safe arrival.

Let’s assume that you, the ship’s doctor, have been woken up to assist a fellow traveler needing medical help. Which assistant would you prefer in this context – a LAM, LLM, or SLM? In this case, an SLM specifically trained in medical knowledge would be the ideal choice. Its specialization in medical terminology and procedures would ensure accurate and efficient assistance.

Finally, imagine you’re interacting with an AI in the ship’s bar. Chances are, this AI would be an LLM. While it may not be as specialized as an SLM or as strategic as an LAM, an LLM’s broad knowledge base and conversational abilities make it perfect for general interactions, such as engaging in casual conversation and even laughing at your jokes. Although, personally, I might prefer waking up another human for company.

Closing Note

As the field of Generative AI progresses, understanding the distinctions between Small Language Models (SLMs) and Large Action Models (LAMs) becomes increasingly important. SLMs offer specialized, efficient solutions for specific use cases, while LAMs bring advanced strategic and autonomous decision-making capabilities, moving us closer to achieving Artificial General Intelligence.

These advancements open many possibilities, each suited to different scenarios and requirements. Whether it’s the focused expertise of an SLM, the broad conversational abilities of an LLM, or the complex reasoning of an LAM, these models are shaping the future of AI in exciting and diverse ways. As we continue to explore and develop these technologies, the potential applications and benefits will only expand, driving innovation across various industries.

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