Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908465160519680 |
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| author | Laptev, Daniil Balagansky, Nikita Aksenov, Yaroslav Gavrilov, Daniil |
| author_facet | Laptev, Daniil Balagansky, Nikita Aksenov, Yaroslav Gavrilov, Daniil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyze Feature Flow to Enhance Interpretation and Steering in Language Models Laptev, Daniil Balagansky, Nikita Aksenov, Yaroslav Gavrilov, Daniil Machine Learning Computation and Language 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. |
| title | Analyze Feature Flow to Enhance Interpretation and Steering in Language Models |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2502.03032 |