Papers by Mike Angstadt
Sparse Feature Coactivation Reveals Causal Semantic Modules in Large Language Models (2026.acl-long)
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Ruixuan Deng, Xiaoyang Hu, Miles Gilberti, Shane Storks, Aman Taxali, Mike Angstadt, Chandra Sripada, Joyce Chai
| Challenge: | Recent work has focused on layerwise interpretations, lacking fine-grained interpretation of specific features and their interaction. |
| Approach: | They identify semantically coherent, context-consistent network components in large language models . they use sparse autoencoders to coactivate sparsity features from a handful of prompts . |
| Outcome: | The proposed model can capture concepts and relations more comprehensively than individual features while maintaining specificity. |