This blog post delves into the fascinating world of large language models (LLMs), exploring how these models store and access factual information. Inspired by recent research from Google DeepMind, we'll embark on a journey through the inner workings of transformers, focusing on the enigmatic multi-layer perceptron's (MLPs) and their role in encoding knowledge. We'll also explore how Google Cloud's Vertex AI, and specifically the Vertex AI Metadata API, can shed light on these intricate mechanisms. I have published my first book, "What Everone Should Know about the Rise of AI" is live now on google play books at Google Play Books and Audio, check back with us at https://theapibook.com for the print versions, go to Barnes and Noble at Barnes and Noble Print Books!
Watch this Google Notebook LM AI generated Podcaset video for this blogpost:
The Curious Case of Mike Tyson
Consider this: you provide an LLM with the prompt "Mike Tyson plays the sport of _______" and it accurately predicts "boxing." This seemingly simple task hints at a complex underlying mechanism. How does the model, with its billions of parameters, manage to store and retrieve such specific facts?
Deep Dive into Transformers
To understand this, we need to peek inside the architecture of transformers, the backbone of LLMs. Transformers consist of two main components:
Attention: This mechanism allows the model to weigh the importance of different parts of the input text, capturing contextual relationships between words. Imagine it as a spotlight that focuses on relevant words while processing a sentence, enabling the model to understand nuances like pronouns and long-range dependencies.
Multi-layer Perceptrons (MLPs): These are the workhorses of the model, responsible for a significant portion of its knowledge encoding capacity. They act as intricate networks within the transformer, processing information and potentially storing factual associations.
Inside the MLPs
MLPs, at their core, perform a series of mathematical operations:
Linear Transformation: This involves multiplying the input vector by a large matrix (the weight matrix) and adding a bias vector. This step can be visualized as projecting the input vector into a higher-dimensional space, where each dimension potentially represents a different feature or concept.
Non-linear Activation: The output of the linear transformation is then passed through a non-linear function, typically ReLU (Rectified Linear Unit). This introduces non-linearity, enabling the model to learn complex patterns and relationships.
Down-projection: Another linear transformation maps the output back to the original dimensionality.
However, interpreting what these computations represent in terms of knowledge storage is a challenging task.
A Toy Example: Encoding "Michael Jordan Plays Basketball"
To illustrate how MLPs might store factual information, let's imagine a simplified scenario:
Assumptions: We'll assume that the model's high-dimensional vector space has specific directions representing concepts like "first name Michael," "last name Jordan," and "basketball."
Encoding: When a vector aligns with a particular direction, it signifies that the vector encodes that concept. For instance, a vector encoding "Michael Jordan" would strongly align with both the "first name Michael" and "last name Jordan" directions.
MLPs in Action: The MLPs would be responsible for recognizing the presence of "Michael Jordan" in the input (perhaps through a combination of neurons acting as an "AND" gate) and activating a neuron that adds the "basketball" direction to the output vector.
Superposition: A Complication and an Opportunity
While our toy example provides a basic intuition, the reality is likely more complex. Research suggests that individual neurons rarely represent single, isolated concepts. Instead, they may participate in a phenomenon called "superposition," where multiple concepts are encoded within the same neuron.
This poses challenges for interpretability but also hints at the incredible capacity of LLMs. Superposition allows models to store far more information than the dimensionality of their vector space would conventionally allow.
Vertex AI and the Metadata API for Transparency and Explainability
Google Cloud's Vertex AI provides a comprehensive suite of tools for building and deploying machine learning models, including LLMs. Vertex AI offers:
Pre-built LLMs: Access to powerful pre-trained language models for various tasks.
Custom model training: The ability to train your own LLMs on specific datasets.
Model explainability tools: Features to help understand and interpret model predictions, which could be valuable for analyzing how LLMs store factual knowledge.
Specifically, the Vertex AI Metadata API can play a crucial role in enhancing transparency and explainability:
Tracking Model Lineage: It can record the entire training process, including the datasets used, hyperparameters, and model versions. This provides a detailed history for auditing and understanding how a model evolved.
Logging Model Artifacts: The API can store intermediate outputs and activations within the MLPs during training. This allows researchers to analyze how different neurons and layers contribute to knowledge representation.
Capturing Feature Importance: By tracking the influence of different input features on model predictions, the Metadata API can help identify which features are most relevant for specific facts or concepts.
By leveraging the Metadata API, researchers and developers can gain deeper insights into the inner workings of LLMs, potentially leading to more interpretable and trustworthy AI systems.
Looking Ahead
This exploration of MLPs and knowledge representation in LLMs provides a glimpse into the intricate workings of these powerful models. As research progresses and tools like Vertex AI evolve, we can expect even more fascinating insights into the mechanisms that enable LLMs to understand and generate human language.
Learn more about this topic on the Google DeepMind team Alignment Forum post part 1 and part 2 on the https://www.youtube.com/@3blue1brown channel.
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