CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Models
<|reference_start|>CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Models: Multimodal foundation models are prone to hallucination, generating outputs that either contradict the input or are not grounded by factual information. Given the diversity in architectures, training data and instruction tuning techniques, there can be large variations in systems' susceptibility to hallucinations. To assess system hallucination robustness, hallucination ranking approaches have been developed for specific tasks such as image captioning, question answering, summarization, or biography generation. However, these approaches typically compare model outputs to gold-standard references or labels, limiting hallucination benchmarking for new domains. This work proposes "CrossCheckGPT", a reference-free universal hallucination ranking for multimodal foundation models. The core idea of CrossCheckGPT is that the same hallucinated content is unlikely to be generated by different independent systems, hence cross-system consistency can provide meaningful and accurate hallucination assessment scores. CrossCheckGPT can be applied to any model or task, provided that the information consistency between outputs can be measured through an appropriate distance metric. Focusing on multimodal large language models that generate text, we explore two information consistency measures: CrossCheck-explicit and CrossCheck-implicit. We showcase the applicability of our method for hallucination ranking across various modalities, namely the text, image, and audio-visual domains. Further, we propose the first audio-visual hallucination benchmark, "AVHalluBench", and illustrate the effectiveness of CrossCheckGPT, achieving correlations of 98% and 89% with human judgements on MHaluBench and AVHalluBench, respectively.<|reference_end|>
@article{sun2024crosscheckgpt:,
    title={CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation
  Models},
    author={Guangzhi Sun, Potsawee Manakul, Adian Liusie, Kunat Pipatanakul, Chao
  Zhang, Phil Woodland, Mark Gales},
    journal={arXiv preprint arXiv:2405.13684},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13684},
    primaryClass={cs.CL}
}