Vedant Gaur

Vedant Gaur

Aspiring engineer and founder with a focus on ML and AI, specifically LLM applications.

Unraveling the Mystery of Superposition in Large Language Models

3 minute read

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In the rapidly evolving field of artificial intelligence, Large Language Models (LLMs) like GPT, BERT, and their successors have emerged as powerful tools capable of understanding and generating human-like text. While these models demonstrate remarkable abilities, their inner workings remain largely opaque. One intriguing theory attempting to explain the efficiency and capability of LLMs is the concept of “superposition” - a phenomenon borrowed from quantum physics to describe how these models might represent information internally.

Fundamentals of Large Language Models

Before delving into superposition, it’s crucial to understand the basics of LLMs:

  1. Architecture: LLMs typically use the transformer architecture, consisting of multiple layers of self-attention and feed-forward neural networks.
  2. Training: These models are trained on vast amounts of text data, learning to predict the next word in a sequence or fill in masked words.
  3. Parameters: Modern LLMs can have billions or even trillions of parameters, which are the adjustable weights and biases within the neural network.
  4. Embeddings: LLMs represent words and concepts as high-dimensional vectors in a semantic space.

Despite their impressive performance, LLMs face a fundamental challenge: how to represent the vast complexity of language and knowledge within a finite number of parameters?

Understanding Superposition

Superposition in LLMs refers to the ability of models to represent more features or concepts than there are neurons or dimensions, by overlapping or combining representations. This concept suggests that LLMs might be able to “pack” more information into their parameters than we might naively expect, by cleverly overlapping or interleaving representations.

To grasp the idea of superposition, consider this analogy: Imagine you have a limited number of drawers to store your clothes. Instead of dedicating each drawer to a single type of clothing, you might efficiently pack multiple types in each drawer, perhaps using different sections or layers within the drawer. This allows you to store more types of clothing than you have drawers.

Evidence for Superposition in LLMs

Several lines of research have provided evidence supporting the superposition hypothesis in LLMs:

  1. Sparse Coding and Feature Disentanglement: Researchers have used techniques from sparse coding to “disentangle” overlapping features in LLM activations, suggesting that LLMs are indeed representing multiple concepts within the same set of neurons.

  2. Linear Probing: Simple linear classifiers can often extract multiple different types of information from the same set of neurons in an LLM, consistent with the idea of superposed representations.

  3. Interference Patterns: When probing for multiple concepts simultaneously, researchers sometimes observe interference effects that are consistent with superposed representations.

The Toy Model Approach

To better understand superposition, researchers have turned to simplified “toy models” - small neural networks trained on synthetic data. A seminal paper in this area is “Toy Models of Superposition” by researchers at Anthropic.

Key findings from this study include:

  • Superposition emerges naturally when models need to represent more features than they have dimensions in their hidden layers.
  • There’s a sharp phase transition between regimes where features are represented separately vs. in superposition.
  • Correlated features tend to collapse into shared dimensions, while uncorrelated features are more likely to be represented in superposition.
  • In the superposition regime, individual neurons become “polysemantic,” responding to multiple unrelated features.

Implications and Future Directions

The superposition hypothesis has several important implications for our understanding and development of LLMs, including efficiency and capacity, interpretability challenges, training dynamics, and architectural implications.

As research in this area progresses, we may gain crucial insights into the inner workings of these powerful models. Understanding superposition could be key to building more efficient, interpretable, and capable AI systems in the future.

Citations: https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/32162028/0e263beb-d62b-4b6d-abc6-ab25d6a88860/paste.txt