METER: Multimodal Hallucination Detection with Mixture of Experts via Tools Ensembling and Reasoning

Overview

METER is a multimodal hallucination detection method guided by the chain of thought, which incorporates domain knowledge to address semantic duality and enhance model inference. You can easily use it for Large Vision-Language Models (LVLMs) in your research experiments.

Preliminaries

METER is supposed to detect the hallucinations on in four categories:

1. Object Hallucination refers to a misperceived presence of objects in an image or conflicts between objects depicted in an image and their corresponding textual descriptions.
2. Attribute Hallucination occurs when the LVLM generates incorrect attribute information or inaccurately describes objects' attributes in an image, such as positions, colors, and actions.
3. Scene-Text Hallucination involves incorrect generation or recognition of text found in various real-world contexts, such as books, signs, banners, and product descriptions.
4. Factuality Hallucination refers to the generation of images or text that contradict established real-world knowledge, violate common sense, or conflict with factual information. All hallucinations are suitable for image-to-text and text-to-image generation tasks.

METER Illustration

The framework of METER is depicted in Figure 1. We appoint mixed experts to collect evidence for hallucination, and the decision-making expert provides the final detection result and explanations.

Figure 1: The framework of METER.

Dataset

We evaluate our method on the MHaluBench dataset.

Quick Start

Step1: Installation for tools

# install Yolo world
pip install ultralytics==8.2.2
pip install openai-clip

download weight: https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-world.pt

# install paddleocr
pip install paddleocr==2.7.3
pip install paddlepaddle-gpu==2.6.1

# install GroundingDINO
git clone https://github.com/IDEA-Research/GroundingDINO.git
cp -r GroundingDINO pipeline/GroundingDINO
cd pipeline/GroundingDINO/
pip install -e .
cd ..

download weight: 
https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth

# install MAERec
git clone https://github.com/Mountchicken/Union14M.git
cp -r Union14M/mmocr-dev-1.x pipeline/mmocr
cd pipeline/mmocr/
pip install -U openmim
mim install mmengine
mim install mmcv
mim install mmdet
pip install timm
pip install -r requirements/albu.txt
pip install -r requirements.txt
pip install -v -e .
cd ..

download weight: https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015_20221101_124139-4ecb39ac.pth

download weight: 
https://github.com/Mountchicken/Union14M/releases/download/Checkpoint/maerec_b_union14m.pth

Step2: Write a configuration file in yaml format Users can easily configure the parameters of METER in a yaml file. The path of the configuration file is EasyDetect/pipeline/config/config.yaml

openai:
  api_key: # your_openai_API_key
  base_url: 
  temperature: 0.2  
  max_tokens: 1024
gemini:
  GOOGLE_API_KEY: # your_gemini_API_key
tool: 
  detect:
    groundingdino_config: # the path of GroundingDINO_SwinT_OGC.py
    model_path: # the path of groundingdino_swint_ogc.pth
    device: cuda:0
    BOX_TRESHOLD: 0.35
    TEXT_TRESHOLD: 0.25
    AREA_THRESHOLD: 0.001
  ocr:
    dbnetpp_config: # the path of dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015.py
    dbnetpp_path: # the path of maerec_b_union14m.py
    maerec_path: # the path of maerec_b.pth
    device: cuda:0
    content: word.number
    cachefiles_path: # the path of cache_files to save temp images
    BOX_TRESHOLD: 0.2
    TEXT_TRESHOLD: 0.25
  google_serper:
    serper_api_key: # your_google_serper_API_key
    snippet_cnt: 10
prompts:
  claim_generate: pipeline/prompts/claim_generate.yaml
  query_generate: pipeline/prompts/query_generate.yaml
  verify: pipeline/prompts/verify.yaml

Step3: Run with the code

python METER.py

Citation

Please cite our repository if you use METER in your work.

    @InProceedings{10.1007/978-981-97-9443-0_24,
    author="Zhang, Ruwen
    and Chen, Jinglu
    and Dai, Mingjie
    and Jiang, Xinyi
    and Hu, Yuxin
    and Liu, Bo
    and Cao, Jiuxin",
    title="METER: Multimodal Hallucination Detection with Mixture of Experts via Tools Ensembling and Reasoning",
    booktitle="Natural Language Processing and Chinese Computing",
    year="2025",
    publisher="Springer Nature Singapore",
    address="Singapore",
    pages="274--286",
    isbn="978-981-97-9443-0"
    }