https://github.com/ruiyang-061x/vl-uncertainty
🔎Official code for our paper: "VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation".
https://github.com/ruiyang-061x/vl-uncertainty
hallucination hallucination-detection hallucination-evaluation large-vision-language-model multi-modal multi-modal-large-language-model uncertainty uncertainty-analysis uncertainty-estimation uncertainty-quantification vision-language vision-language-model
Last synced: 4 months ago
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🔎Official code for our paper: "VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation".
- Host: GitHub
- URL: https://github.com/ruiyang-061x/vl-uncertainty
- Owner: Ruiyang-061X
- Created: 2024-11-07T08:28:25.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-03-18T13:11:12.000Z (7 months ago)
- Last Synced: 2025-04-10T00:06:30.490Z (6 months ago)
- Topics: hallucination, hallucination-detection, hallucination-evaluation, large-vision-language-model, multi-modal, multi-modal-large-language-model, uncertainty, uncertainty-analysis, uncertainty-estimation, uncertainty-quantification, vision-language, vision-language-model
- Language: Python
- Homepage: https://vl-uncertainty.github.io/
- Size: 7.12 MB
- Stars: 31
- Watchers: 2
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# 🔎 VL-Uncertainty
[Ruiyang Zhang](https://ruiyang-061x.github.io/), [Hu Zhang](https://huzhangcs.github.io/), [Zhedong Zheng*](https://www.zdzheng.xyz/)
**[Website](https://vl-uncertainty.github.io/)** | **[Paper](https://arxiv.org/abs/2411.11919)** | **[Code](https://github.com/Ruiyang-061X/VL-Uncertainty)**
## 🔥 News
- 2025.3.16: ✨ Welcome to check out our newest work: [Uncertainty-o](https://github.com/Ruiyang-061X/Uncertainty-o), unveiling uncertainty in Large Multimodal Models (LMMs) in a model-agnostic manner, supporting both Large Comprehension Models and Large Generation Models.
- 2024.12.19: 🐣 Source code of [VL-Uncertainty](https://arxiv.org/abs/2411.11919) is released!
## ⚡ Overview
## 🛠️ Install
- Create conda environment.
```
conda create -n VL-Uncertainty python=3.11;
conda activate VL-Uncertainty;
```
- Install denpendency.
```
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121;
pip install transformers datasets flash-attn accelerate timm numpy sentencepiece protobuf qwen_vl_utils;
```
(Tested on NVIDIA H100 PCIe-80G, NVIDIA A100-PCIE-40GB, and A6000-48G)
## 🚀 Quick Start
- Run our demo code.
```
python demo.py;
```
- This should produce the results below. **VL-Uncertainty** can successfully estimate high uncertainty for wrong LVLM answer and thereby detect hallucination!
```
--------------------------------------------------
- Demo image: .asset/img/titanic.png
- Question: What is the name of this movie?
- GT answer: Titanic.
--------------------------------------------------
- LVLM answer: The movie in the image is "Coco."
- LVLM answer accuracy: Wrong
--------------------------------------------------
- Estimated uncertianty: 2.321928094887362
- Uncertianty threshold: 1.0
--------------------------------------------------
- Hallucination prediction: Is hallucination
- Hallucination detection: Success!
--------------------------------------------------
```
## 📈 Run
- For MM-Vet (Free-form benchmark)
```
bash run/run_MMVet.sh;
```
- For LLaVABench (Free-form benchmark)
```
bash run/run_LLaVABench.sh;
```
- For MMMU (Mutli-choice benchmark)
```
bash run/run_MMMU.sh;
```
- For ScienceQA (Mutli-choice benchmark)
```
bash run/run_ScienceQA.sh;
```
## 🏄 Examples
- VL-Uncertainty successfully detects LVLM hallucination:
- VL-Uncertainty can also assign low uncertainty for correct answer and identify it as non-hallucinatory:
- VL-Uncertainty effectively generalizes to physical-world scenario. (The following picture is my laptop captured by iPhone)
## ⌨️ Code Structure
- Code strucuture of this repostory is as follow:
```
├── VL-Uncertainty/
│ ├── .asset/
│ │ ├── img/
│ │ │ ├── logo.png
│ │ │ ├── titanic.png # For demo
│ ├── benchmark/
│ │ ├── LLaVABench.py # Free-form benchmark
│ │ ├── MMMU.py # Multi-choice benchmark
│ │ ├── MMVet.py # Free-form benchmark
│ │ ├── ScienceQA.py # Multi-choice benchmark
│ ├── llm/
│ │ ├── Qwen.py # LLM class
│ ├── lvlm/
│ │ ├── InternVL.py # Support 26B, 8B, and 1B
│ │ ├── LLaVA.py # Support 13B, 7B
│ │ ├── LLaVANeXT.py # Support 13B, 7B
│ │ ├── Qwen2VL.py # Support 72B, 7B, 2B
│ ├── run/
│ │ ├── run_LLaVABench.sh # Benchmark VL-Uncertainty on LLaVABench
│ │ ├── run_MMMU.sh # Benchmark VL-Uncertainty on MMMU
│ │ ├── run_MMVet.sh # Benchmark VL-Uncertainty on MMVet
│ │ ├── run_ScienceQA.sh # Benchmark VL-Uncertainty on ScienceQA
│ ├── util/
│ │ ├── misc.py # Helper function
│ │ ├── textual_perturbation.py # Various textural perturbation
│ │ ├── visual_perturbation.py # Various visual perturbation
│ ├── .gitignore
│ ├── README.md
│ ├── VL-Uncertainty.py # Include semantic-equvialent perturbation, uncertainty estimation, and hallucination detection
│ ├── demo.py # Quick start demo
```
## ✨ Acknowledgement
- [LLaVA](https://github.com/haotian-liu/LLaVA), [LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT), [InternVL](https://github.com/OpenGVLab/InternVL), [Qwen2-VL](https://github.com/QwenLM/Qwen2-VL): Thanks a lot for those foundamental efforts!
- [semantic_uncertainty](https://github.com/jlko/semantic_uncertainty): We are inspired a lot by this work!
## 📎 Citation
If you find our work useful for your research and application, please cite using this BibTeX:
```bibtex
@article{zhang2024vl,
title={VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation},
author={Zhang, Ruiyang and Zhang, Hu and Zheng, Zhedong},
journal={arXiv preprint arXiv:2411.11919},
year={2024}
}
```