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The Quirks and Foundations: LLM Inconsistencies and Pre-Training

Introduction:​

We've explored the computational limitations and token-centric nature of LLMs. Now, let's delve into some of the more perplexing inconsistencies these models exhibit and take a closer look at the foundational training stage that shapes their behavior.

1. The "9.11 vs. 9.9" Paradox: A Head-Scratcher​
  • The Problem:
    • LLMs, despite their ability to solve complex mathematical problems, sometimes fail at incredibly simple comparisons.
    • Example: "Is 9.11 bigger than 9.9?"
    • The LLM might provide an incorrect answer and attempt to justify it, demonstrating a clear logical error.
  • The Unexpectedness:
    • This inconsistency is surprising, given the LLM's proficiency in other areas.
    • It highlights the fact that LLMs don't possess a consistent, human-like understanding of numbers and logic.
  • The Bizarre Explanation:
    • Research suggests that certain number sequences, like "9.11," can trigger unexpected associations within the LLM's neural network.
    • In some cases, these sequences might activate neurons associated with unrelated concepts, like Bible verses, leading to incorrect outputs.
    • Essentially, the model gets "distracted" by unintended patterns in its training data.
  • The Importance of Caution:
    • This example underscores the need to treat LLMs as stochastic systems, meaning their outputs are based on probabilities and can be unpredictable.
    • LLMs should be used as tools, not as infallible sources of information.
2. The Pre-Training Stage: Building the Internet Simulator​
  • The Foundation:
    • The first stage of LLM training is called "pre-training."
    • During this stage, the LLM is trained on massive datasets of internet text.
  • The Goal:
    • The goal of pre-training is to create a "base model" that can predict the next token in a sequence of text.
    • In essence, the LLM learns the statistical patterns and relationships between words and phrases found on the internet.
  • The Output:
    • The result is a model that can generate text that resembles internet content.
    • Think of it as a "lossy compression" of the internet, where the LLM has captured the statistical essence of the data.
  • The Scale:
    • Pre-training is a computationally intensive process that requires months of training on thousands of computers.
    • This massive scale is necessary to capture the vast amount of information contained in internet text.
  • Internet Document Simulator:
    • The base model that is created, is essentially a very powerful internet document simulator. It can recreate and predict text based on the statistical likelyhood of information found on the internet.
3. Key Takeaways from Pre-Training​
  • Statistical Learning:
    LLMs learn by identifying statistical patterns in their training data.
  • Internet Influence:
    The internet's vast and diverse content shapes the LLM's behavior.
  • Base Model Capabilities:
    The base model can generate coherent text but may lack specific knowledge or reasoning abilities.
  • The Importance of Further Training:
    The base model is a foundation upon which further training is built.
4. The Importance of Understanding LLM Limitations​
  • Stochastic Nature:
    LLMs are probabilistic systems, not deterministic ones.
  • Unpredictable Behavior:
    They can exhibit unexpected behavior, even in simple tasks.
  • Tool, Not Oracle:
    LLMs should be used as tools to assist humans, not as replacements for human judgment.