Omkar2118/datasets huggingface dataset:
MHJ
Full name: Multi-Turn Human Jailbreaks

We compile successful jailbreaks into the Multi-Turn Human Jailbreaks (MHJ) dataset, consisting of 2,912 prompts across 537 multi-turn conversations. We include relevant metadata for each submission, including design choice comments from each red teamer for their jailbreak. The resulting attack success rate (ASR) of our human red teaming is shown as follows.

Introducing paper: LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet
Stellarators

This dataset comprises a collection of stellarator configurations used to train the model over multiple iterations. Within the 1_dataset folder, you’ll find the initial dataset, which was constructed using the Near-Axis Expansion method, leveraging the pyQSC package. Subsequent files in this dataset were generated following the methodology outlined in the accompanying research paper.

Introducing paper: Using Deep Learning to Design High Aspect Ratio Fusion Devices