It is impossible to ignore the revolution generated by tools like ChatGPT, DALL-E, Midjourney, or GitHub Copilot — and the possibility of being able to write a piece of text, and have artificial intelligence (AI) return a completely coherent response or image, and above all — be extremely useful was something unthinkable a while ago.
What these tools have in common is that many of them use an LLM as a basis, that is, a Large Language Model (not to be confused with the master’s degree in law, LLM). For example, the LLM that ChatGPT and Dall-E employ is GPT, which is now in its fourth iteration; while Google’s Bard was recently updated to PaLM 2. In future developments, different AI tools can be integrated into an LLM app platform.
LLMs are relatively new machine learning models, whose first version only appeared in 2018, although as is usual within this area, it was only for research purposes and internal use within the organizations that created them. Only now have they become widespread among the general public, especially with the arrival of the tools mentioned above.
What are Large Language Models (LLM)?
An LLM is another type of machine learning model. This means that its most basic function is to find patterns in new data, based on previously performed training.
LLMs are trained with an immense amount of data (one reason why they are “big”), coming from various sources, and in different formats. For example, GitHub Copilot — a tool that allows you to generate code from a simple instruction — was trained with the billions of lines of code that are hosted in the different repositories that are on the platform. On the other hand, GPT-4 was trained with information from a good part of the internet.
Regarding “finding patterns in new data”, what these types of models do, in simple terms, is predict the words to use, based on a provided context.
The advantage that these large models have over any other that is capable of processing human language is that when trained they not only learn from the words themselves, but also the context and semantics behind them. This allows the model, for example, to understand those subtle differences that may exist in the same word, and to be able to generate coherent and grammatically correct text.
That is why these have become super popular for generating and processing all types of text. Their ability to understand human language is such that they have become very good translators, code writers (not to mention programmers), instructors, and much more.
In the evolution of LLM so far, we know some of its main architectures. They are:
- Transformer Architecture: First introduced in 2017, this is the basis for the modern LLM. Attention mechanisms and the concept of self-attention are two things introduced by this model. Both influence the relationship between words in a sentence to form a certain context.
- GPT Series (Generative Trained Transformers): Starting with GPT-1 in 2018, and continuing through previous versions, the GPT series is the most popular AI architecture in the world. With millions of downloads in its early days, it is clear that the GPT hype is the most interesting and applicable to most people.
- BERT (Bidirectional Encoder Representation from Transformers): This is Google’s LLM architecture introduced in 2018. This architecture focuses on understanding the context of a text.
- XLNet: Built based on a transformer architecture, XLNet combines two important elements; autoregressive and autoencoding. As a result, dependencies between all words in the text can be detected unambiguously.
There are other architectures, but the four above are the most popular ones.
Klu, all-in-one LLM App Platform to build, evaluate, and optimize GPT-4 Apps
All these LLM architectures can be integrated on a single platform called Klu, a platform that organizes leading LLM architecture providers such as Open AI, Anthropic, Azure Open AI, and Google. Klu can be thought of as a liaison between these LLM providers and user data such as CRM, databases, ticketing systems, etc., and executing user wishes.
With Klu, users can revolutionize the use of AI applications and possibly expand them because Klu allows users to optimize all the generative AI features offered by LLM architectures.
Klu Studio encourages users to go further by prototyping generative AI features and it can be done in just a few minutes. Its strong integration with user data makes it easy for users to get contextual information which is very valuable in using generative AI features for final results.
And finally, with Klu users can scale to any LLM provider while avoiding lock-in, thus comparing models in real-world deployments is possible.
In conclusion, LLM is the backbone of AI and the architectures within it. They get more perfect over time. However, their integration into user data is another matter and when they are combined for real projects in the field, a connecting platform is needed and that is Klu.