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Safety Alignment

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Showing 101–125 of 288 papers

TitleStatusHype
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning—0
DiaBlo: Diagonal Blocks Are Sufficient For FinetuningCode0
BitBypass: A New Direction in Jailbreaking Aligned Large Language Models with Bitstream CamouflageCode0
Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models—0
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data SynthesisCode0
Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap—0
SafeCOMM: What about Safety Alignment in Fine-Tuned Telecom Large Language Models?—0
AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language ModelsCode0
EVOREFUSE: Evolutionary Prompt Optimization for Evaluation and Mitigation of LLM Over-Refusal to Pseudo-Malicious Instructions—0
Seeing the Threat: Vulnerabilities in Vision-Language Models to Adversarial Attack—0
OVERT: A Benchmark for Over-Refusal Evaluation on Text-to-Image ModelsCode0
SOSBENCH: Benchmarking Safety Alignment on Scientific Knowledge—0
PoisonSwarm: Universal Harmful Information Synthesis via Model Crowdsourcing—0
SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety—0
VSCBench: Bridging the Gap in Vision-Language Model Safety CalibrationCode0
Beyond Safe Answers: A Benchmark for Evaluating True Risk Awareness in Large Reasoning ModelsCode0
Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models—0
Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment?—0
Safety Alignment via Constrained Knowledge Unlearning—0
Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary—0
One Model Transfer to All: On Robust Jailbreak Prompts Generation against LLMsCode0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning—0
From Evaluation to Defense: Advancing Safety in Video Large Language Models—0
Mitigating Fine-tuning Risks in LLMs via Safety-Aware Probing OptimizationCode0
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