In the previous article, we discussed what LAM is.

One key word to remember when explaining LAM is 'Execution Power.'

So, you might be wondering what the difference is between LLM (Large Language Model), the technology behind ChatGPT, which gained widespread attention since late 2023, and LAM (Large Action Model) that we discussed in the previous article.

Today, we'll explain the differences between LAM and LLM, the AI technology that received the most attention before LAM's release.

If you'd like to know more about LAM, you can check the link below.

What is LAM? Imagining the future of AI and humans with Large Action Model

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What is the difference between LLM and LAM?

How can we explain LLM in the simplest way?

For example, ChatGPT, which uses LLM, primarily uses statistical techniques to process language. LLM generates the most suitable response based on a vast amount of information provided by the user. In other words, while LLM excels at 'information generation' and text-based Q&A based on extensive data, it has limitations in predicting actual behaviors or responding in real-time. It struggles to adapt quickly to changes in the real world and to handle tasks beyond what was expected.

On the other hand, LAM has the ability to predict user behavior based on various inputs (text, images, videos, etc.) and to execute actions accordingly. LAM doesn't just generate information; it analyzes the user's situation and takes the appropriate action, complementing the shortcomings of LLM.

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Here's a brief summary of the differences between LAM and LLM:

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Why do we need LAM?

As mentioned earlier, LAM provides 'execution capability' that LLM lacks. While LLM focuses on analyzing text and generating results, LAM is a model that determines 'how to act' based on the information provided. For example, while LLM can provide information about a specific situation, it cannot take real-world action when actually facing that situation.

In contrast, LAM can assess the situation in real-time, analyze it, and take action accordingly.

We need AI not just to provide information-based answers but to determine how to act and execute in reality based on the provided information. LAM has the execution capability to solve various problems users might face, going beyond simply answering questions to provide real, actionable help in real-world situations where immediate action is required.

In other words, while LAM can analyze text and provide appropriate responses like LLM, it can also analyze various inputs like images and videos, find solutions, and perform complex actions, which is why it’s considered an important technology in advancing AI. That’s why many companies, including Enhans, are actively working on its development.

How do I choose between LAM and LLM based on my needs?

As outlined in the table comparing LAM and LLM, the two models have different purposes and uses, so it's important to choose  the model that best suits your needs.

If you primarily want to use information retrieval, natural language processing, or conversational systems, LLM is suitable. For example, LLM performs powerfully when you want to analyze complex text or summarize insights and key points from large datasets.

However, for more complex use cases or when 'execution' is important, LAM is better choice. For example, LAM would be more effective when you need a system that predicts user behavior and automatically executes appropriate actions based on that, LAM is more effective.

In summary, LLM is more suitable for information delivery, while LAM is more appropriate when execution is the goal.

Conclusion

Today, we looked at the differences between LLM and LAM.

To effectively utilize LAM, we believe that various models, or multiple agents, need to work together to bring us closer to our ideal AI.

In the next article, we'll discuss the important concept of 'AI agents', which is also important in explaining LAM.