ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models
<|reference_start|>ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models: Hallucination poses a significant challenge for multimodal large language models (MLLMs). However, existing benchmarks for evaluating hallucinations are static, which can lead to potential data contamination. This paper introduces ODE, an open-set, dynamic protocol for evaluating object existence hallucinations in MLLMs. Our framework employs graph structures to model associations between real-word concepts and generates novel samples for both general and domain-specific scenarios. The dynamic combination of concepts, along with various combination principles, ensures a broad sample distribution. Experimental results show that MLLMs exhibit higher hallucination rates with ODE-generated samples, effectively avoiding data contamination. Moreover, these samples can also be used for fine-tuning to improve MLLM performance on existing benchmarks.<|reference_end|>
@article{tu2024ode:,
    title={ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language
  Models},
    author={Yahan Tu, Rui Hu, Jitao Sang},
    journal={arXiv preprint arXiv:2409.09318},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09318},
    primaryClass={cs.CL cs.CV}
}