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From Query to Response: Understanding the LLM Journey

Introduction:​

We've explored the inner workings of LLMs. Now, let's trace the path from your query to the LLM's response, understanding the roles of pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL) along the way.

1. The Query: From Text to Tokens​
  • Tokenization:​
    • When you enter a query, it's first broken down into tokens, which are the basic units of language for LLMs.
    • This process is similar to how we break down sentences into words or syllables.
  • Conversation Protocol:​
    • Your query is formatted according to a conversation protocol, which helps the LLM understand the context of the interaction.
    • This protocol maintains a history of the conversation, allowing the LLM to provide relevant responses.
  • Token Sequence:​
    • The formatted query is then converted into a one-dimensional sequence of tokens, which is the input the LLM processes.
2. The Response: A Simulation of Human Expertise​
  • Token Autocompletion:​
    • The LLM generates a response by predicting the next tokens in the sequence.
    • This is similar to how your phone suggests words as you type.
  • Supervised Fine-Tuning (SFT):​
    • The LLM's response is a simulation of a human data labeler at a company like OpenAI.
    • These data labelers are trained to provide ideal assistant responses to various prompts.
    • The LLM learns to imitate these responses through SFT.
  • Neural Network Simulation:​
    • The LLM's response is generated by a neural network, which is a complex mathematical function.
    • This simulation is not a perfect replica of human thought, as LLMs have different cognitive strengths and weaknesses.
3. The Impact of Reinforcement Learning (RL): Thinking Models​
  • Beyond Imitation:​
    • LLMs trained with RL, known as "thinking models," go beyond simple imitation.
    • They develop their own reasoning strategies and problem-solving techniques.
    • This leads to more creative and insightful responses.
  • Emergent Thinking:​
    • RL can lead to the emergence of novel thinking patterns in LLMs.
    • These patterns may resemble human internal monologues or even surpass human capabilities.
  • Verifiable vs. Unverifiable Domains:​
    • RL is particularly effective in verifiable domains (e.g., math, coding), where there are objective "correct" answers.
    • Its effectiveness in unverifiable domains (e.g., creative writing) is still being explored.
4. Responsible Use: Understanding Limitations​
  • Hallucinations:​
    • LLMs can generate factually incorrect or nonsensical responses.
    • These "hallucinations" are a result of the LLM's statistical nature and limited understanding of the world.
  • Swiss Cheese Model:​
    • LLMs have uneven capabilities, excelling in some areas while failing in others.
    • They may struggle with simple tasks like counting or basic arithmetic.
  • Tool, Not Oracle:​
    • Treat LLMs as tools, not as infallible sources of information.
    • Verify their responses and use critical thinking.
  • Check and Verify:​
    • Always check the output of the LLM, especially when dealing with factual information.
    • Use LLMs for inspiration and first drafts but take responsibility for the final product.
5. The Future: Exciting Possibilities​
  • Advanced Reasoning:​
    • RL is pushing LLMs towards more sophisticated reasoning abilities.
    • We may see LLMs develop novel analogies and problem-solving strategies.
  • Continuous Improvement:​
    • The field of LLMs is rapidly evolving, with ongoing research and development.
    • LLMs are becoming more powerful, versatile, and reliable.