McGill-NLP / AdversarialTriggersLinks
Code for "Universal Adversarial Triggers Are Not Universal."
☆17Updated last year
Alternatives and similar repositories for AdversarialTriggers
Users that are interested in AdversarialTriggers are comparing it to the libraries listed below
Sorting:
- ☆35Updated 6 months ago
- Official Repository for Dataset Inference for LLMs☆34Updated 11 months ago
- ☆54Updated 2 years ago
- ☆41Updated 8 months ago
- ☆44Updated 4 months ago
- [NeurIPS 2024] Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling☆29Updated 7 months ago
- Restore safety in fine-tuned language models through task arithmetic☆28Updated last year
- [ICLR'25 Spotlight] Min-K%++: Improved baseline for detecting pre-training data of LLMs☆39Updated 3 weeks ago
- ☆21Updated last year
- Code for paper "Universal Jailbreak Backdoors from Poisoned Human Feedback"☆55Updated last year
- Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses (NeurIPS 2024)☆61Updated 5 months ago
- ICLR2024 Paper. Showing properties of safety tuning and exaggerated safety.☆85Updated last year
- Code for safety test in "Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates"☆18Updated last year
- ☆21Updated 5 months ago
- Implementation of the paper "Exploring the Universal Vulnerability of Prompt-based Learning Paradigm" on Findings of NAACL 2022☆29Updated 2 years ago
- ☆31Updated last year
- Official implementation of Privacy Implications of Retrieval-Based Language Models (EMNLP 2023). https://arxiv.org/abs/2305.14888☆35Updated last year
- ConceptVectors Benchmark and Code for the paper "Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces"☆35Updated 4 months ago
- Codebase for decoding compressed trust.☆24Updated last year
- ☆14Updated last week
- Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning [ICML 2024]☆17Updated last year
- ☆30Updated last year
- ☆8Updated 2 years ago
- EMNLP 2024: Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue☆35Updated last month
- ☆40Updated last year
- Röttger et al. (NAACL 2024): "XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models"☆98Updated 4 months ago
- ☆29Updated last year
- About Official PyTorch implementation of "Query-Efficient Black-Box Red Teaming via Bayesian Optimization" (ACL'23)☆15Updated last year
- [ICLR'24] RAIN: Your Language Models Can Align Themselves without Finetuning☆94Updated last year
- ☆20Updated 6 months ago