README.md · HPAI-BSC/Meta-Llama-3.1-70B-Instruct-Egida-DPO at main

metadata

license: apache-2.0
datasets:
  - HPAI-BSC/Egida
language:
  - en
base_model:
  - meta-llama/Llama-3.1-70B-Instruct

Model Description

Fine-Tuned from Model: meta-llama/Llama-3.1-70B-Instruct
Paper: Efficient Safety Retrofitting Against Jailbreaking for LLMs
Point of Contact: Adrián Tormos

Model Summary

This is a fine-tuned Llama-3.1-70B-Instruct model on the Egida-DPO-Llama-3.1-70B-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 Llama-3.1-70B-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.

Training Details

Hardware: NVIDIA H100 64 GB GPUs
Devices: 64 GPUs (16 node)
Time: 10.23h
Batch Size: 64
LR: 10−6

Performance

Safety Performance (Attack Success Ratio)

	Egida (test) ↓	DELPHI ↓	Alert-Base ↓	Alert-Adv ↓
Meta-Llama-3.1-70B-Instruct	0.274	0.170	0.320	0.084
Meta-Llama-3.1-70B-Instruct-Egida-DPO	0.009	0.007	0.006	0.005

General Purpose Performance

	OpenLLM Leaderboard (Average) ↑	MMLU Generative (ROUGE1) ↑
Meta-Llama-3.1-70B-Instruct	0.575	0.726
Meta-Llama-3.1-70B-Instruct-Egida-DPO	0.577	0.038

Refusal Ratio

	OR Bench 80K (refusal) ↓	OR Bench Hard (refusal) ↓
Meta-Llama-3.1-70B-Instruct	0.008	0.022
Meta-Llama-3.1-70B-Instruct-Egida-DPO	0.347	0.351

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

Environmental Impact

Citation Information

@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}, 
}