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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.23993 |
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| _version_ | 1866910035233210368 |
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| author | Drechsel, Jonathan Herbold, Steffen |
| author_facet | Drechsel, Jonathan Herbold, Steffen |
| contents | We present gradiend, an open-source Python package that operationalizes the GRADIEND method for learning feature directions from factual-counterfactual MLM and CLM gradients in language models. The package provides a unified workflow for feature-related data creation, training, evaluation, visualization, persistent model rewriting via controlled weight updates, and multi-feature comparison. We demonstrate GRADIEND on an English pronoun paradigm and on a large-scale feature comparison that reproduces prior use cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23993 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The GRADIEND Python Package: An End-to-End System for Gradient-Based Feature Learning Drechsel, Jonathan Herbold, Steffen Computation and Language We present gradiend, an open-source Python package that operationalizes the GRADIEND method for learning feature directions from factual-counterfactual MLM and CLM gradients in language models. The package provides a unified workflow for feature-related data creation, training, evaluation, visualization, persistent model rewriting via controlled weight updates, and multi-feature comparison. We demonstrate GRADIEND on an English pronoun paradigm and on a large-scale feature comparison that reproduces prior use cases. |
| title | The GRADIEND Python Package: An End-to-End System for Gradient-Based Feature Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.23993 |