Jan 31, 2024
3 mins

Demystifying Large Language Models: School Internship at Predict42

In today's world, Large Language Models (LLMs) like ChatGPT have become ubiquitous tools, seamlessly integrating into our daily interactions. But behind their seemingly effortless ability to generate human-quality text and converse intelligently lies a simple core problem as well as the sophisticated mechanism involved in solving it. Let's delve into the inner workings of these remarkable AI systems and uncover the secrets that make them so powerful.

Demystifying Large Language Models: School Internship at Predict42

The Pre-Training Phase: Laying the Foundation

LLMs excel at predicting the next word in a sequence, a deceptively simple task that belies their remarkable capabilities. To achieve this, they are trained on an enormous dataset, typically encompassing over 10 terabytes of text. This vast repository of linguistic information serves as a training ground, allowing the model to adjust its parameters, analogous to tuning the sensitivity of its senses, to grasp the nuances of natural language and its patterns. This process, known as "pre-training," establishes a deep understanding of the language's structure and the relationships between words.

Diagram visualizing the task an LLM performs

Beyond Single Words: Understanding Context and Intent

While LLMs normally provide a single word as a response/continuation, their true power lies in their iterative calling. Because of their large amount of training data and some algorithms we didn't talk about, they can comprehend the context and iteratively create coherent and logical sentences.

diagram showing the iterative process of LLMs where the output of one prediction becomes part of the input of the next one

This becomes apparent when you interact with them, as they can respond to questions, follow instructions, and even engage in open-ended conversations. The key to achieving this lies in the "fine-tuning" stage, a secondary training process that refines the model's capabilities.

The Fine-Tuning Process: Shaping Helpful Interactions

Fine-tuning involves exposing the LLM to a smaller, more specialized dataset that is tailored to the specific task, such as chatbot interactions or language translation. This allows the model to adapt its knowledge to the nuances of the target domain, enabling it to provide more meaningful and relevant responses.

To elicit helpful and informative responses from LLMs, we prompt them with queries that mimic user interactions, such as questions or instructions. The model then engages in a process of iterative prediction, generating likely next words and assessing their suitability. Initially, these responses may not be entirely accurate, as the model may fall back on patterns learned during pre-training, such as finishing the sequence with more questions instead of an answer.

Example of an AI answering a question with more questions

However, this is where the beauty of fine-tuning shines through. By providing the model with feedback on the desired outcome, we guide it towards producing more helpful and relevant responses. This continuous loop of prediction, assessment, and refinement effectively shapes the model's behavior, transforming it into a valuable assistant that can seamlessly engage in natural conversations and provide meaningful assistance.

Conclusion: The Power of Language Unleashed

Large Language Models have ushered in a new era of human-AI interaction, unlocking the potential for seamless communication and collaboration. By understanding the underlying principles of pre-training and fine-tuning, we can appreciate the remarkable capabilities of these AI systems and envision the transformative impact they will continue to make on our lives. From chatbots that provide personalized assistance to language models that translate with unprecedented accuracy, LLMs are poised to revolutionize the way we interact with technology and the world around us. However, this explanation of their inner workings should also have highlighted the limitations of LLMs, how they basically hallucinate the most likely response given a sequence of words and that if the training data is wrong or harmful the predictions will be as well.

-- Jonathan Spork (left-most on thumbnail)

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