Year: 2024
Type: preprint
Abstract: Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. To address this, we introduce a new task: multi-hop multimodal claim verification. This task challenges models to reason over multiple pieces of evidence from diverse sources, including text, images, and tables, and determine whether the combined multimodal evidence supports or refutes a given claim. To study this task, we construct MMCV, a large-scale dataset comprising 16k multi-hop claims paired with multimodal evidence, generated and refined using large language models, with additional input from human feedback. We show that MMCV is challenging even for the latest state-of-the-art multimodal large language models, especially as the number of reasoning hops increases. Additionally, we establish a human performance benchmark on a subset of MMCV. We hope this dataset and its evaluation task will encourage future research in multimodal multi-hop claim verification.
Source: arXiv (Cornell University)
Authors: Wang, Haoran, Rangapur, Aman, Xu, Xiongxiao, Liang, Yueqing, Gharwi, Haroon, Yang, Carl, Shu, Kai
Institutions: Emory University, Illinois Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Research: SaTC: CORE: Small: Targeting Challenges in Computational Disinformation Research to Enhance Attribution, Detection, and Explanation, G3997495584, CAREER: Towards Fairness in the Real World under Generalization, Privacy and Robustness Challenges, Understanding Diabetes Heterogeneity via Mining Multimodality Interconnected Data
OpenAlex ID: W4404451269