Papers with superficial unlearning

2 papers
FaithUn: Toward Faithful Forgetting in Language Models by Investigating the Interconnectedness of Knowledge (2025.emnlp-main)

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Challenge: Existing methods for unlearning undesirable knowledge have overlooked complexity and interconnectedness of knowledge, authors say . previous studies have neglected the complex nature of knowledge and neglected its internal dependencies.
Approach: They propose a new concept called superficial unlearning to evaluate faithfulness of unlearning in knowledge QA settings.
Outcome: The proposed method shows significant effectiveness in real-world knowledge QA settings.
Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing (2025.acl-long)

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Challenge: Existing knowledge editing algorithms are prone to generating original knowledge . despite the fact that many models achieve near-perfect performance, superficial editing remains a challenge .
Approach: They propose to use "**superficial editing**" to describe the phenomenon . they investigate the internal mechanisms of the attention module and their corresponding left singular vectors .
Outcome: The proposed method can modify specific knowledge in a pretrained large language model while ensuring that unrelated knowledge remains unaffected.

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