Scaling and evaluating sparse autoencoders
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In § 6, they mention hat they only use a context length of 64. Most of the really interesting behavior I've seen in language models happens in long context where the model can do in-context learning. Why would we believe or not believe that these interpretability methods carry over to longer context situations, i.e. Why do we expect or not expect that autoencodos trained on of short sequences would carry over to longer ones?
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Thinking Summary
The user is asking about the context length limitation in the sparse autoencoder paper (64 tokens) and whether the interpretability methods would carry over to longer contexts. This is a great question about the generalization of these methods. Key points to consider: 1. Why they used short context (computational efficiency) 2. What changes with longer context (in-context learning, more complex behaviors) 3. Reasons to expect generalization 4. Reasons to be skeptical 5. What kinds of features might be missed I should give a balanced view that acknowledges both potential for generalization and important limitations.
This is an excellent question that gets at a fundamental limitation of the paper. The 64-token context length is indeed quite restrictive compared to the interesting emergent behaviors we see in longer contexts. Let me think through both sides:
Reasons to expect some generalization:
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Basic features are context-invariant: Many low-level features (syntax, basic semantics, entity recognition) likely activate similarly regardless of context length. A "Python code" feature or "discussing economics" feature probably works similarly at token 64 vs token 1024.
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Compositional structure: If the model builds complex behaviors by composing simpler features, and SAEs capture those atomic features well, then longer-context behaviors might still decompose into these same building blocks.
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Local attention patterns dominate: Even in long contexts, much of the computation is still local - most tokens attend strongly to nearby tokens. Features learned from 64-token windows capture these local patterns.
Reasons to be skeptical:
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In-context learning is fundamentally different: ICL involves the model learning new input-output mappings on the fly. The features that implement "store this example," "retrieve relevant examples," and "apply learned pattern" might be completely invisible in short contexts.
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Long-range dependencies create new features: Features like "callback to paragraph 3's main claim" or "maintaining consistency with character traits established 500 tokens ago" simply cannot exist in 64-token windows.
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Attention head specialization: Some attention heads might specialize for long-range retrieval in ways that are never activated in short contexts. The SAEs would miss these entirely.
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Different computational strategies: Models might use entirely different algorithms for tasks depending on available context. A 64-token window might use shallow pattern matching while 2000 tokens enables more sophisticated reasoning.
I think the most likely outcome is that these SAEs capture a subset of features that are necessary but not sufficient for understanding long-context behavior. They'd miss the architectural innovations that make transformers powerful for long sequences - the very features that distinguish them from models that can only handle short contexts.