Papers with defensive modules

1 papers
Data to Defense: The Role of Curation in Aligning Large Language Models Against Safety Compromise (2025.emnlp-main)

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Challenge: Recent studies have identified a vulnerability in large language models (LLMs) during customization.
Approach: They propose an adaptive data curation approach that allows any text to be curated to enhance its effectiveness in counteracting harmful samples during customization.
Outcome: The proposed approach reduces compromising effects and generates 100% safe responses.

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