LINE: LLM-based Iterative Neuron Explanations for Vision Models
Interpreting individual neurons in deep neural networks is a crucial step towards understanding their complex decision-making processes and ensuring AI safety. Despite recent progress in neuron labeling, existing methods often limit the search space to predefined concept vocabularies or produce overly specific descriptions that fail to capture higher-order, global concepts. We introduce LINE, a novel, training-free iterative approach tailored for open-vocabulary concept labeling in vision models. Operating in a strictly black-box setting, LINE leverages a large language model and a text-to-image generator to iteratively propose and refine concepts in a closed loop, guided by activation hi...
| Authors | Vladimir Zaigrajew, Michał Piechota, Gaspar Sekula et al. |
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| Primary category | cs.CV |
| Published | 9 April 2026 |
| Last updated at source | 12 May 2026 |
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