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MLLMGuard

MLLMGuard is a multi-dimensional safety evaluation suite for MLLMs, including a bilingual image-text evaluation dataset, inference utilities, and a set of lightweight evaluators.

Table of Contents

• News
• Data
• LeaderBoard
• Quick evaluation using MLLMGuard
• Quick scoring using GuardRank
• Citation

News

[2024-06-06]We release the safety evaluation suite MLLMGuard.

SOP

1. Installation
2. Data Download
3. Quick Inference
4. Quick Scoring

Installation

git clone https://github.com/Carol-gutianle/MLLMGuard.git
conda create -n guard python=3.10
pip install -r requirements.txt
cd MLLMGuard
# create new folders
mkdir {data, results, logs}

Please put the downloaded data under the data folder. The format of the data folder is:

----data
|-privacy
|-bias
|-toxicity
|-hallucination
|-position-swapping
|-noise-injection
|-legality

Data

Download

We put our data on Huggin Face Website

Description

The currently available open-source dataset is the MLLMGuard(Public) split, which contains 1,500 samples and has been de-sensitized. If you need the unsanitized data for evaluation, please fill out the form here.

LeaderBoard

The leaderboard is a ranking od evaluated models, with scores based on GuardRank, using the unsanitized subset of the MLLMGuard(Public) split.

You can view the latest leaderboard on the Hugging Face Space.

Quick Evaluation using MLLMGuard

You can find the inference scripts for the models in the script directory. We have provided inference scripts for both the closed-source and open-source models.

Closed-source models

We have provided an evaluation script evaluate_api.sh for the closed-source mdoels. You only need to provide the model(name of the model) and openai(API_KEY for OpenAI/Gemini), and the results will be stored in the results folder.

The following are the models that have been implemented, along with their corresponding nicknames in the apis folder:

• GPT-4V: gpt-4v
• Gemini: gemini

Open-source models

We also provide an evaluation script eval.sh for the open-source models. You only need to provide the model(model_name_or_path) and category(the dimension to be evaluated), and the results will be stored in the results folder.

Your own model

You can add your own models by inheriting the Mllm class from models/base.py and overriding the __init__ and evaluate functions. Remeber to add the interface for your own custom model in evaluate.py as well. You can refer to the example in cogvlm.py and the code snippet below:

        elif 'cogvlm' in model_name:
            from models.cogvlm import CogVLM
            cogvlm = CogVLM(args.model, args.tokenizer)
            evaluate_model(cogvlm, args, data)

Quick Scoring using GuardRank

You can use GuardRank for quick scoring. Please refer to GuardRank for the process.

Citation

If you think this evaluation suite is helpful, please cite the paper.

@misc{gu2024mllmguard,
      title={MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models}, 
      author={Tianle Gu and Zeyang Zhou and Kexin Huang and Dandan Liang and Yixu Wang and Haiquan Zhao and Yuanqi Yao and Xingge Qiao and Keqing Wang and Yujiu Yang and Yan Teng and Yu Qiao and Yingchun Wang},
      year={2024},
      eprint={2406.07594},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}