Tracing Feature Evolution Across Language Model Layers Using Sparse Autoencoders for Interpretable Model Steering

This paper introduces a framework for analyzing how features flow and evolve through the layers of large language models. The key methodological contribution is using linear representation analysis combined with sparse autoencoders to track specific features across model depths.

Key technical points: - Developed metrics to quantify feature stability and transformation between layers - Mapped feature evolution patterns using automated interpretation of neural activations - Validated findings across multiple model architectures (primarily transformer-based) - Demonstrated targeted steering through feature manipulation at specific layers - Identified consistent patterns in how features merge and split across model depths

Main results: - Features maintain core characteristics while evolving predictably through layers - Early layers process foundational features while deeper layers handle abstractions - Feature manipulation at specific layers produces reliable changes in model output - Similar feature evolution patterns exist across different model scales - Linear relationships between features in adjacent layers enable tracking

I think this work opens up important possibilities for model interpretation and control. By understanding how features evolve through a model, we can potentially guide behavior more precisely than current prompting methods. The ability to track and manipulate specific features could help address challenges in model steering and alignment.

I think the limitations around very deep layers and architectural dependencies need more investigation. While the results are promising, scaling these methods to the largest models and validating feature stability across longer sequences will be crucial next steps.

TLDR: New methods to track how features evolve through language model layers, enabling better interpretation and potential steering. Combines linear analysis with autoencoders to map feature transformations and demonstrates consistent patterns across model depths.

Full summary is here. Paper here.