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Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
🤗 Upvotes: 41 | cs.LG, cs.CL
Authors:
Daniil Laptev, Nikita Balagansky, Yaroslav Aksenov, Daniil Gavrilov
Title:
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
Arxiv:
http://arxiv.org/abs/2502.03032v2
Abstract:
We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined inter-layer feature links. By using a data-free cosine similarity technique, we trace how specific features persist, transform, or first appear at each stage. This method yields granular flow graphs of feature evolution, enabling fine-grained interpretability and mechanistic insights into model computations. Crucially, we demonstrate how these cross-layer feature maps facilitate direct steering of model behavior by amplifying or suppressing chosen features, achieving targeted thematic control in text generation. Together, our findings highlight the utility of a causal, cross-layer interpretability framework that not only clarifies how features develop through forward passes but also provides new means for transparent manipulation of large language models.
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By Jingwen Liang, Gengyu Wang🤗 Upvotes: 41 | cs.LG, cs.CL
Authors:
Daniil Laptev, Nikita Balagansky, Yaroslav Aksenov, Daniil Gavrilov
Title:
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
Arxiv:
http://arxiv.org/abs/2502.03032v2
Abstract:
We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined inter-layer feature links. By using a data-free cosine similarity technique, we trace how specific features persist, transform, or first appear at each stage. This method yields granular flow graphs of feature evolution, enabling fine-grained interpretability and mechanistic insights into model computations. Crucially, we demonstrate how these cross-layer feature maps facilitate direct steering of model behavior by amplifying or suppressing chosen features, achieving targeted thematic control in text generation. Together, our findings highlight the utility of a causal, cross-layer interpretability framework that not only clarifies how features develop through forward passes but also provides new means for transparent manipulation of large language models.