Researchers use toy ReLU networks on synthetic data The study employs small rectified linear unit (ReLU) networks trained on artificially generated datasets with sparse input features to explore internal representations. These models are intentionally minimal to isolate the phenomenon under investigation. The approach allows precise control over feature sparsity and network size. [1]
Superposition describes representing more features than dimensions The authors define superposition as the ability of a model to encode a number of distinct features that exceeds the number of its internal dimensions. This occurs when multiple features share the same representational space. The concept challenges the intuition that dimensionality limits feature capacity. [1]
Sparse features enable compression beyond linear models When input features are sparse, superposition permits the network to compress information more efficiently than a comparable linear model would. The compression reduces the number of parameters needed to capture the data distribution. This advantage is highlighted as a key benefit of nonlinear representations. [1]
Interference arises as a cost of superposition The paper notes that overlapping feature representations can cause interference, requiring the network to apply nonlinear filtering to separate them. This interference can degrade performance if not properly managed. The authors emphasize the trade‑off between compression and the need for additional processing. [1]
The work contributes to interpretability research in AI By isolating superposition in controlled toy models, the study aims to improve understanding of how large language models might reuse internal dimensions. Findings may inform techniques for probing and visualizing neural network internals. The research is part of Anthropic’s broader interpretability program. [1]
The full paper is publicly available online The detailed methodology, experiments, and theoretical analysis are presented in a paper hosted at transformer‑circuits.pub, linked in the article. Readers can access the complete study for deeper technical insight. [1]