Scaling and evaluating sparse autoencoders
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Is it correct to understand the sentence
We choose a layer near the end of the network, which should contain many features without being specialized for next-token predictions
as follows:
- computation occurs as data passesthrough the layers of the network, so later layers have more "refined" views of the relevant data
- on the other hand, since the final layers are specifically trained to do next-token prediction, if those layers were used for interpretability, the internal states of the model may be less accessible
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Thinking Summary
The user is asking about a specific sentence from the paper about sparse autoencoders. They're trying to understand why the authors chose a layer near the end (but not at the very end) of the network for their experiments. The user's interpretation is basically correct. Let me confirm and expand on this: 1. Yes, later layers tend to have more refined/processed representations because computation occurs sequentially through layers 2. Yes, the very final layers are specialized for the specific task (next-token prediction) which might make them less useful for finding general interpretable features The key insight is that there's a trade-off: - Too early in the network: features might be too raw/low-level - Too late in the network: features might be too task-specific (overly specialized for predicting the next token) - Sweet spot near the end but not at the end: features are refined and high-level but still general-purpose This is a common pattern in interpretability work - the penultimate or near-final layers often contain the most useful representations for understanding what the model has learned.
Yes, your understanding is correct! The authors are describing a trade-off when selecting which layer to analyze:
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Later layers have richer representations: As activations flow through the network, each layer builds upon previous computations, creating increasingly sophisticated and abstract features. Later layers have had more opportunity to combine and refine information.
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Final layers become task-specialized: The very last layers are heavily optimized for the specific output task (next-token prediction in this case). Their representations become increasingly tailored to producing the exact logits needed for vocabulary predictions, potentially at the cost of maintaining more general, interpretable features.
This creates a "sweet spot" near the end of the network where you get:
- Features that have undergone substantial processing and abstraction
- Features that haven't yet been fully transformed into task-specific prediction machinery
The paper mentions they chose layer 56 out of ~67 layers for GPT-4 (about 5/6 through) and layer 8 out of 12 for GPT-2 (3/4 through). This positioning aims to capture high-level semantic features while avoiding the extreme task specialization of the final layers.
This principle appears in other interpretability work too - for instance, when extracting features for downstream tasks, penultimate layers often work better than final layers precisely because they maintain more general-purpose representations.