How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference

Unveiling the Magic Behind Large Language Models (LLMs): A Two-Part Exploration

Large Language Models (LLMs) often appear magical, but their inner workings are surprisingly systematic. This two-part series demystifies LLMs, explaining their construction, training, and refinement into the AI systems we use today. Inspired by Andrej Karpathy's insightful (and lengthy!) YouTube video, this condensed version provides the core concepts in a more accessible format. While Karpathy's video is highly recommended (800,000 views in just 10 days!), this 10-minute read distills the key takeaways from the first 1.5 hours.

Part 1: From Raw Data to Base Model

LLM development involves two crucial phases: pre-training and post-training.

1. Pre-training: Teaching the Language

Before generating text, an LLM must learn language structure. This computationally intensive pre-training process involves several steps:

• Data Acquisition and Preprocessing: Massive, diverse datasets are gathered, often including sources like Common Crawl (250 billion web pages). However, raw data requires cleaning to remove spam, duplicates, and low-quality content. Services like FineWeb offer preprocessed versions available on Hugging Face.

• Tokenization: Text is converted into numerical tokens (words, subwords, or characters) for neural network processing. GPT-4, for example, utilizes 100,277 unique tokens. Tools like Tiktokenizer visualize this process.

• Neural Network Training: The neural network learns to predict the next token in a sequence based on context. This involves billions of iterations, adjusting parameters (weights) via backpropagation to improve prediction accuracy. The network's architecture dictates how input tokens are processed to generate outputs.

The resulting base model understands word relationships and statistical patterns but lacks real-world task optimization. It functions like an advanced autocomplete, predicting based on probability but with limited instruction-following capabilities. In-context learning, using examples within prompts, can be employed, but further training is necessary.

2. Post-training: Refining for Practical Use

Base models are refined through post-training using smaller, specialized datasets. This isn't explicit programming but rather implicit instruction through structured examples.

Post-training methods include:

• Instruction/Conversation Fine-tuning: Teaches the model to follow instructions, engage in conversations, adhere to safety guidelines, and refuse harmful requests (e.g., InstructGPT).
• Domain-Specific Fine-tuning: Adapts the model for specific fields (medicine, law, programming).

Special tokens are introduced to delineate user input and AI responses.

Inference: Generating Text

Inference, performed at any stage, evaluates model learning. The model assigns probabilities to potential next tokens and samples from this distribution, creating text not explicitly in the training data but statistically consistent with it. This stochastic process allows for varied outputs from the same input.

Hallucinations: Addressing False Information

Hallucinations, where LLMs generate false information, arise from their probabilistic nature. They don't "know" facts but predict likely word sequences. Mitigation strategies include:

• "I don't know" Training: Explicitly training the model to recognize knowledge gaps through self-interrogation and automated question generation.
• Web Search Integration: Extending knowledge by accessing external search tools, incorporating results into the model's context window.

LLMs access knowledge through vague recollections (patterns from pre-training) and working memory (information in the context window). System prompts can establish a consistent model identity.

Conclusion (Part 1)

This part explored the foundational aspects of LLM development. Part 2 will delve into reinforcement learning and examine cutting-edge models. Your questions and suggestions are welcome!

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