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cs.LG2026
Perturbation-based Regional Interpretability through Subtraction Mapping (PRISM): naming-error dissociations in language models and post-stroke aphasia
Xiang Guan, Roger D. Newman-Norlund, Yong Yang +8
Mechanistic interpretability of large language models lacks spatially resolved, falsifiable tools for testing whether internal components are specialized for distinct cognitive ope…
cs.LG2026
Stroke Lesions as a Rosetta Stone for Language Model Interpretability
Julius Fridriksson, Roger D. Newman-Norlund, Saeed Ahmadi +10
Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language function remain limited. Current i…
cs.LG2019
Thick-Net: Parallel Network Structure for Sequential Modeling
Yu-Xuan Li, Jin-Yuan Liu, Liang Li +1
Recurrent neural networks have been widely used in sequence learning tasks. In previous studies, the performance of the model has always been improved by either wider or deeper str…