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ING-VP Bench

introduction

This repository includes the evaluation code for the corresponding paper"ING-VP: MLLMs cannot Play Easy Vision-based Games Yet"

Overview

We propose ING-VP benchmark, a new interactive game-based vision planning benchmark designed to measure the multi-step reasoning and spatial imagination capabilities of Multimodal Large Language Models (MLLMs). In this work, ING-VP features 6 distinct games, encompassing 300 levels, each with 6 unique configurations. In our settings, a single model engages in over 60,000 rounds of interaction. The benchmark framework allows for multiple comparison settings, including image-only vs. text-only inputs, single-step vs. multi-step reasoning, and with-history vs. without-history conditions, offering valuable insights into the model’s capabilities. With our ING-VP, we reveal that current MLLMs generally lack spatial imagination and multi-step planning abilities, and offer a new perspective on the capability requirements for MLLMs.

introduction

How to use?

Setup

To get started with the ING-VP benchmark, first install the necessary dependencies by executing the following command:

git clone https://github.com/Thisisus7/ING-VP.git
pip install -r requirements.txt

Inference

Four settings of multi-step and one-step at once:

bash run.sh

Evaluation

All outputs will be automatically computed and saved. The final results can be found in the outputs/multi_step/final, outputs/one_step/final and outputs/summary folders.

You can also evaluate the model's performance manually by executing the following command:

python ./src/summary.py

How to add your model?

  • Add the model class in src/model.py.
  • Import the model in src/multi_step/infer.py and src/one_step/infer.py.
  • Update the MODEL variable in run.sh to enable selection of different MLLMs for evaluation.

Citation

@misc{zhang2024ingvpmllmsplayeasy,
      title={ING-VP: MLLMs cannot Play Easy Vision-based Games Yet}, 
      author={Haoran Zhang and Hangyu Guo and Shuyue Guo and Meng Cao and Wenhao Huang and Jiaheng Liu and Ge Zhang},
      year={2024},
      eprint={2410.06555},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.06555}, 
}