Papers with safety training techniques

2 papers
A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models (2024.findings-acl)

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Challenge: Large Language Models (LLMs) have demonstrated capabilities for generating content that could be deemed harmful.
Approach: They conduct a comprehensive analysis of existing studies on jailbreaking LLMs and their defense techniques.
Outcome: The proposed techniques underperform existing white-box attacks and include special tokens significantly affects the likelihood of successful attacks.
Can Textual Unlearning Solve Cross-Modality Safety Alignment? (2024.findings-emnlp)

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Challenge: integrating new modalities into large language models creates new attack surface . existing safety training techniques like SFT and RLHF are not feasible in multi-modal settings .
Approach: They explore whether unlearning in the textual domain can be effective for cross-modality safety alignment.
Outcome: The proposed approach reduces the Attack Success Rate (ASR) to less than 8% and preserves the utility.

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