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Extracting Concepts from GPT-4
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4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: OpenAI
This OpenAI research applies concept extraction and sparse interpretability methods directly to GPT-4, a frontier model, making it notable for scaling interpretability techniques beyond smaller research models.
Metadata
Importance: 72/100blog postprimary source
Summary
OpenAI researchers present work on extracting human-interpretable concepts from GPT-4's internal representations using sparse autoencoders or similar dictionary learning methods. The research aims to identify meaningful features encoded in the model's activations, advancing mechanistic interpretability of large language models.
Key Points
- •Uses unsupervised methods to identify discrete, human-interpretable concepts within GPT-4's neural activations
- •Applies dictionary learning or sparse decomposition techniques to map activation space to interpretable features
- •Represents a scaling of mechanistic interpretability methods to frontier-scale production models
- •Contributes to understanding how large LLMs internally represent knowledge and concepts
- •Part of broader OpenAI interpretability research agenda aimed at making AI systems more transparent
Cited by 3 pages
| Page | Type | Quality |
|---|---|---|
| Is Interpretability Sufficient for Safety? | Crux | 49.0 |
| Sleeper Agent Detection | Approach | 66.0 |
| Sparse Autoencoders (SAEs) | Approach | 91.0 |
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Extracting Concepts from GPT-4 | OpenAI
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OpenAI
Table of contents
The challenge of interpreting neural networks
Our research progress: large scale autoencoder training
Limitations
Looking ahead, and open sourcing our research
June 6, 2024
Publication
Extracting concepts from GPT‑4
We used new scalable methods to decompose GPT‑4’s internal representations into 16 million oft-interpretable patterns.
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We currently don't understand how to make sense of the neural activity within language models. Today, we are sharing improved methods for finding a large number of "features"—patterns of activity that we hope are human interpretable. Our methods scale better than existing work, and we use them to find 16 million features in GPT‑4. We are sharing a paper(opens in a new window), code(opens in a new window), and feature visualizations(opens in a new window) with the research community to foster further exploration.
The challenge of interpreting neural networks
Unlike with most human creations, we don’t really understand the inner workings of neural networks. For example, engineers can directly design, assess, and fix cars based on the specifications of their components, ensuring safety and performance. However, neural networks are not designed directly; we instead design the algorithms that train them. The resulting networks are not well understood and cannot be easily decomposed into identifiable parts. This means we cannot reason about AI safety the same way we reason about something like car safety.
In order to understand and interpret neural networks, we first need to find useful building blocks for neural computations. Unfortunately, the neural activations inside a language model activate with unpredictable patterns, seemingly representing many concepts simultaneously. They also activate densely, meaning each activation is always firing on each input. But real world concepts are very sparse—in any gi
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