GitHub - junyangwang0410/HaELM: An automatic MLLM hallucination detection framework

1. Installing

2. Preparing

3. Training

We provide the hallucination training dataset in "data/train_data.jsonl" and the manually labeled validation set in "data/eval_data.jsonl". If you want to:

see here.

4. Interface

We provide interface templates populated by the output of mPLUG-Owl in "LLM_output/mPLUG_caption.jsonl".

5. Citation

@article{wang2023evaluation,
  title={Evaluation and Analysis of Hallucination in Large Vision-Language Models},
  author={Wang, Junyang and Zhou, Yiyang and Xu, Guohai and Shi, Pengcheng and Zhao, Chenlin and Xu, Haiyang and Ye, Qinghao and Yan, Ming and Zhang, Ji and Zhu, Jihua and others},
  journal={arXiv preprint arXiv:2308.15126},
  year={2023}
}