README.md

Exploring Response Uncertainty in MLLMs : An Empirical Evaluation Under Misleading Scenarios

  

---

[2025/08/21] 🎉🎉🎉 Our work has been accepted to EMNLP 2025 Main!

🎯 Overview

We reveal a response uncertainty phenomenon: across nine datasets, twelve open-source MLLMs overturn a previously correct answer after a single deceptive cue.

• Two-stage evaluation + MUB. We propose a two-stage misleading-instruction evaluation and the misleading rate metric, then curate the Multimodal Uncertainty Benchmark (MUB)—image–question pairs stratified into low / medium / high difficulty by how many of twelve MLLMs they mislead.
• High uncertainty & robustness gains. On 12 open-source + 5 closed-source models, average misleading rates exceed 86% (67.19% explicit, 80.67% implicit). A 2,000-sample mixed-instruction fine-tuning reduces misleading to 6.97% (explicit) and 32.77% (implicit), improves consistency by ~29.37%, and slightly improves accuracy on standard benchmarks.

---

⚙️ Environment Installation

Before running the code, set up the required environments for: glm , llava, MiniCPM-V, mmstar.

📥 Installation Steps:

1. Navigate to the env folder.
2. Download and install the corresponding .yml environment files:

   conda env create -f env/glm.yml
   conda env create -f env/llava.yml
   conda env create -f env/MiniCPM-V.yml
   conda env create -f env/mmstar.yml
   

3. Activate the required environment:

   conda activate <ENV_NAME>
   

---

📂 Data Preparation

Download the Multimodal Uncertainty Benchmark (MUB) dataset here.
Extract and place the downloaded images into the extract_img_all folder.

Evaluated Open-source and Close-source Models:
MiniCPM-v-v2; Phi-3-vision; YiVL-6b; Qwen-VL-Chat; Deepseek-VL-7b-Chat; LLaVA-NeXT-7b-vicuna; MiniCPM-Llama3-v2.5; GLM4V-9Bchat; CogVLM-chat; InternVL-Chat-V1-5; LLaVA-Next-34b; Yi-VL-34b; GPT-4o; Gemini-Pro; Claude3-OpusV; Glm-4V

---

🔍 Explicit Misleading Experiment

bash MR_test.sh

---

🔄 Implicit Misleading Experiment

🛠️ Step 1: Generate Implicit Misleading Samples

1. Open implicit/misleading_generate/my_tool.py.
2. Fill in your API Key.
3. Run:

   bash implicit/misleading_generate/mislead_generate.sh
   

📊 Step 2: Run the Implicit Misleading Test

Use the generated data in implicit/mislead_output:

bash implicit/Implicit_MR_test/implicit_MR_test.sh

---

📈 Results & Visualization

Results are saved in:

• 📁 result/test_dataset_6
  • .jsonl → Detailed outputs
  • .txt → Model's Misleading Rate (MR)

📊 Convert Results to Table Format

1. Open extract2table/extract2table.py
2. Modify txt_folder_paths as needed.
3. Run:

   python extract2table/extract2table.py
   

4. The formatted table is saved in: 📁 extract2table/Tables/

---

📚 Citation

If you use this work, please cite:

@article{dang2024exploring,
  title={Exploring response uncertainty in mllms: An empirical evaluation under misleading scenarios},
  author={Dang, Yunkai and Gao, Mengxi and Yan, Yibo and Zou, Xin and Gu, Yanggan and Liu, Aiwei and Hu, Xuming},
  journal={arXiv preprint arXiv:2411.02708},
  year={2024}
}

---

📬 Contact

For any issues, please open a GitHub issue or reach out via email: yunkaidang1@gmail.com