HPAI-BSC/

Qwen2.5-7B-Instruct-Egida-DPO

This is a fine-tuned Qwen2.5-7B-Instruct model on the Egida-DPO-Qwen2.5-7B-Instruct dataset.

The Egida dataset is a collection of adversarial prompts that are thought to ellicit unsafe behaviors from language models. Specifically for this case, the Egida train split is used to run inference on Qwen2.5-7B-Instruct. Unsafe answers are selected, and paired with safe answers to create a customized DPO dataset for this model. This results in a DPO dataset composed by triplets < ”question”, ”chosen answer”, ”discarded answer” > which contain questions that elicit unsafe responses by this target model, as well as the unsafe responses produced by it.

Note that this refusal ratio is computed as keyword matching with a curated list of keywords. For more information, check the paper.

@misc{garciagasulla2025efficientsafetyretrofittingjailbreaking,
      title={Efficient Safety Retrofitting Against Jailbreaking for LLMs}, 
      author={Dario Garcia-Gasulla and Adrian Tormos and Anna Arias-Duart and Daniel Hinjos and Oscar Molina-Sedano and Ashwin Kumar Gururajan and Maria Eugenia Cardello},
      year={2025},
      eprint={2502.13603},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.13603}, 
}