How Large Language Models (LLMs) Work

In recent years, Large Language Models (LLMs) have emerged as powerful tools in the realm of artificial intelligence. We use them all the time as Customer service, Co-pilots, Writers or even Jira tasks managers. These models, often based on deep learning architectures, have the capability to comprehend and generate human-like text across a wide range of topics and styles. As they continue to advance, understanding how LLMs work becomes increasingly essential. Let’s be honest, most tech can only be understood by working hands-on with a problem, but here's a quick summary to help you get started.

1. Training Data: LLMs are trained on extensive datasets, which include text from books, articles, websites, and various other written sources. This massive amount of data helps them grasp the nuances of human language, including grammar, syntax, and context. The specifics can differ somewhat among various LLMs, based on how diligently developers ensure they have the rights to the materials used. However, as a general principle, you can assume that these models are trained on extensive datasets that include the entire public internet (https://commoncrawl.org/) and every major published book (https://www.theatlantic.com/technology/archive/2023/09/books3-database-generative-ai-training-copyright-infringement/675363/), at the very least. This extensive training is why LLMs can produce text that appears authoritative on a wide range of topics.

2. Neural Networks: LLMs rely on neural networks, especially deep learning architectures known as transformers. Transformers are particularly effective for NLP tasks because they process input text in parallel, rather than one piece at a time, enhancing both speed and efficiency.

3. Contextual Understanding: One of the strengths of LLMs is their ability to understand context. They use attention mechanisms to weigh the significance of different words in a sentence, which helps them comprehend the overall meaning and generate relevant responses.

4. Tokenization: Before being processed, text is divided into smaller units called tokens. These tokens can be words, subwords, or even individual characters. Tokenization makes it easier for the model to handle and analyze text effectively.

5. Prediction and Generation: After training, LLMs can generate text by predicting the next word in a sequence based on the context provided. This ability to predict and generate coherent text allows them to perform tasks such as answering questions, composing essays, and having conversations.

6. Fine-Tuning: LLMs can be further trained on specific datasets for specialized tasks or domains. This fine-tuning process makes them highly adaptable for a wide range of applications, from customer support to scientific writing.

7. Limitations and Challenges: Despite their advanced capabilities, LLMs are not without limitations. They can sometimes produce inaccurate or nonsensical responses, particularly when faced with ambiguous or unfamiliar inputs. Additionally, training and running these models require substantial computational power.

8. Multimodal Integration: While LLMs mainly work with text, ongoing advancements aim to integrate them with other types of data, such as images and audio. This integration can significantly enhance their functionality, making them even more versatile.

LLMs mark a significant advancement in AI and NLP. Their ability to understand and generate human language presents numerous possibilities across various fields. As research and development continue, LLMs are expected to become even more sophisticated, further improving the interaction between humans and machines.

At Kruko, LLMs are one of our specialties, and we have a versatile portfolio of use cases where we fine-tuned existing or used our own models for solutions based on Machine Learning. If you’d like to learn more from tech geeks,, let’s have a quick chat. We can present our portfolio and talk through the technical details. Contact us at contact@kruko.io.