4 papers
Reasoning Models Know What's Important, and Encode It in Their Activations
Yaniv Nikankin, Martin Tutek, Tomer Ashuach +2
Language models often solve complex tasks by generating long reasoning chains, consisting of many steps with varying importance. While some steps are crucial for generating the fin…
Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness
Tomer Ashuach, Shai Gretz, Yoav Katz +2
Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether large language models possess si…
CRISP: Persistent Concept Unlearning via Sparse Autoencoders
Tomer Ashuach, Dana Arad, Aaron Mueller +2
As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become pa…
REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space
Tomer Ashuach, Martin Tutek, Yonatan Belinkov
Language models (LMs) risk inadvertently memorizing and divulging sensitive or personally identifiable information (PII) seen in training data, causing privacy concerns. Current ap…