Papers by Yonatan Slutzky

1 papers
Mamba Knockout for Unraveling Factual Information Flow (2025.acl-long)

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Challenge: Recent work has introduced Mamba-based SSM architectures that rival Transformer performance in various settings.
Approach: They propose to use attentional interpretability techniques originally developed for Transformers to trace how information is transmitted and localized across tokens and layers.
Outcome: The proposed model disentangles how distinct features enable token-to-token information exchange or enrich individual tokens, thus offering a unified lens to understand Mamba internal operations.

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