@inproceedings{grover-etal-2024-navigating,
    title = "Navigating Hallucinations for Reasoning of Unintentional Activities",
    author = "Grover, Shresth  and
      Vineet, Vibhav  and
      Rawat, Yogesh S",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.findings-emnlp.565/",
    doi = "10.18653/v1/2024.findings-emnlp.565",
    pages = "9666--9680",
    abstract = "In this work we present a novel task of understanding unintentional human activities in videos. We formalize this problem as a reasoning task under zero-shot scenario, where given a video of an unintentional activity we want to know why it transitioned from intentional to unintentional. We first evaluate the effectiveness of current state-of-the-art Large Multimodal Models on this reasoning task and observe that they suffer from hallucination. We further propose a novel prompting technique, termed as Dream of Thoughts (DoT), which allows the model to navigate through hallucinated thoughts to achieve better reasoning. To evaluate the performance on this task, we also introduce three different specialized metrics designed to quantify the models reasoning capability. We perform our experiments on three datasets, OOPs, UCF-Crimes, and ReUAct, and our findings show that DOT prompting technique is able to outperform standard prompting, while minimizing hallucinations."
}