AI BriefingExtracting Concepts from GPT-4
Key point
OpenAI has unveiled a new method that decomposes GPT-4's internal representations into 16 million interpretable patterns.
Details
The inner workings of neural networks remain a black box. Existing neural network activation patterns are unpredictable and dense, making it difficult to understand how specific concepts are represented. This is a major obstacle to verifying AI safety as logically as, say, automotive safety.
To address this, Sparse Autoencoders (SAE) are used. SAEs identify a small number of 'features' within a neural network that have a significant impact on specific outputs, uncovering sparse activation patterns that are easier for humans to understand.
OpenAI developed a new methodology that also works on large-scale models, successfully finding as many as 16 million features in GPT-4. This research demonstrates superior scalability compared to existing techniques, and also introduces new metrics for evaluating feature quality.
To support follow-up research from the research community, OpenAI has released the related paper, code, and a visualization tool for directly examining the features.
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