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Safety Alignment

Papers

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Showing 201–225 of 288 papers

TitleStatusHype
Finding Safety Neurons in Large Language Models—0
From Evaluation to Defense: Advancing Safety in Video Large Language Models—0
From Judgment to Interference: Early Stopping LLM Harmful Outputs via Streaming Content Monitoring—0
"Haet Bhasha aur Diskrimineshun": Phonetic Perturbations in Code-Mixed Hinglish to Red-Team LLMs—0
Internal Activation as the Polar Star for Steering Unsafe LLM Behavior—0
Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations—0
Jailbreak Attacks and Defenses Against Large Language Models: A Survey—0
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models—0
JBFuzz: Jailbreaking LLMs Efficiently and Effectively Using Fuzzing—0
JULI: Jailbreak Large Language Models by Self-Introspection—0
Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning—0
Learn to Disguise: Avoid Refusal Responses in LLM's Defense via a Multi-agent Attacker-Disguiser Game—0
Llama-3.1-Sherkala-8B-Chat: An Open Large Language Model for Kazakh—0
LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models—0
LLM-Safety Evaluations Lack Robustness—0
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper—0
LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B—0
LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models—0
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming—0
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models—0
Model Card and Evaluations for Claude Models—0
Model-Editing-Based Jailbreak against Safety-aligned Large Language Models—0
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch—0
More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment—0
Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture—0
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