How might LLMs store facts | Chapter 7, Deep Learning

Summary

This video explores the inner workings of large language models like GPT-3, focusing on how they store knowledge and facts. It explains the roles of attention mechanisms and multi-layer perceptrons (MLPs) in processing high-dimensional vectors and how facts, like 'Michael Jordan plays basketball,' are encoded. Through matrix multiplication and non-linear functions, these models store complex relationships in a vast vector space. The video also introduces the concept of 'superposition,' where multiple features are stored through combinations of neurons. Overall, it delves into how these models scale, store information, and enhance performance, with implications for AI interpretability and training methods.

Takeaways

• 😀 Large language models (LLMs) can memorize facts, like 'Michael Jordan plays basketball,' suggesting they encode specific knowledge about individuals and their domains.
• 😀 MLPs (Multi-Layer Perceptrons) in transformers play a key role in storing facts and encoding knowledge beyond simple word meanings.
• 😀 A vector's interaction with different directions in high-dimensional space can represent complex features, like first names, last names, or sports.
• 😀 A single MLP operation involves two matrix multiplications and a nonlinear activation function (like ReLU) to update vector representations.
• 😀 The first step in MLP processing is matrix multiplication, which maps vectors into new directions, representing different features like 'Michael' or 'Jordan.'
• 😀 Nonlinear functions like ReLU (rectified linear unit) help transform linear outputs, making the encoding of concepts more distinct, for example, encoding 'Michael Jordan' cleanly as a single entity.
• 😀 The second matrix multiplication in an MLP adds further features (like 'basketball') to the encoded vector, depending on the activation of neurons.
• 😀 High-dimensional spaces, especially in large models like GPT-3, allow for the storage of many features by utilizing nearly perpendicular directions, enabling efficient representation of complex ideas.
• 😀 The concept of superposition explains how multiple features can be encoded simultaneously by a combination of neurons, rather than being represented by a single neuron.
• 😀 Scaling the size of the model (like increasing the number of dimensions) allows for the storage of exponentially more independent features, contributing to the performance and scalability of models like GPT-3.

Q & A

What is the key idea behind how large language models store facts?

Large language models store facts not in isolated locations, but across many layers of the model, represented by vectors in high-dimensional spaces. The multi-layer perceptron (MLP) helps process these vectors and combine different pieces of information to encode knowledge.

How do vectors in a high-dimensional space represent knowledge?

Vectors in high-dimensional spaces represent knowledge by encoding words, concepts, and relationships between them. Each word is transformed into a vector that captures not just its meaning but also its context in relation to other words, allowing the model to make predictions based on learned patterns.

What role does the multi-layer perceptron (MLP) play in large language models?

The MLP processes information through multiple layers, transforming input vectors and extracting patterns. This allows the model to combine different features and store complex knowledge in a distributed manner across the network, helping with tasks like language generation.

What is the concept of superposition in the context of neural networks?

Superposition refers to the idea that instead of each neuron representing a single, distinct feature, multiple neurons combine to represent more complex features or facts. This makes knowledge harder to interpret directly, as individual features are distributed across many neurons.

Why is it significant that vectors in high-dimensional spaces can be nearly perpendicular?

When vectors are nearly perpendicular, they can represent different independent ideas without overlap. In high-dimensional spaces, this allows models to store exponentially more independent features, enabling them to process and store much more information than the number of dimensions might suggest.

How does the scaling of dimensions improve model performance?

As the number of dimensions increases in large language models, the capacity to store independent knowledge grows exponentially. This allows models to represent more features and improve their performance, explaining why larger models like GPT-3 can scale more effectively.

What is the Johnson-Lindenstrauss lemma and how does it relate to language models?

The Johnson-Lindenstrauss lemma suggests that high-dimensional spaces allow for the compression of data while preserving distances between points. In the context of language models, it implies that as dimensions increase, the model can store more vectors that are nearly perpendicular, thus encoding more independent information.

How does the model handle storing many ideas within the same space?

The model uses nearly perpendicular vectors to store multiple ideas in the same space. This way, despite limited dimensions, it can represent many different concepts, leveraging the relationships between vectors to encode complex knowledge.

What is the challenge in interpreting knowledge in large language models?

Interpreting knowledge in large language models is challenging because facts are represented as superpositions of features across many neurons. Unlike simple models where each neuron might correspond to a distinct feature, complex models like GPT-3 use overlapping neuron activations to represent a wide variety of information.

How does the optimization process mentioned in the script affect the vectors?

The optimization process nudges the vectors towards being more perpendicular to each other. After many iterations, this process reduces the angle between vectors, creating a narrow range of nearly perpendicular vectors that can store more independent features efficiently.