Summary of Piecing It All Together: Verifying Multi-hop Multimodal Claims, by Haoran Wang et al.
Piecing It All Together: Verifying Multi-Hop Multimodal Claims
by Haoran Wang, Aman Rangapur, Xiongxiao Xu, Yueqing Liang, Haroon Gharwi, Carl Yang, Kai Shu
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Existing claim verification datasets often lack complexity, failing to require systems to perform nuanced reasoning or effectively interpret diverse evidence types. To address this, we introduce the novel task of multi-hop multimodal claim verification (MMCV), which challenges models to reason over multiple pieces of multimodal evidence (text, images, tables) and determine whether the combined evidence supports or refutes a given claim. We construct MMCV, a large-scale dataset comprising 15k multi-hop claims paired with multimodal evidence, generated and refined using large language models, with human feedback input. Our results show that even state-of-the-art multimodal LLMs struggle with MMCV, particularly as the number of reasoning hops increases. A human performance benchmark is established on a subset of MMCV. This dataset and evaluation task aim to encourage future research in multimodal multi-hop claim verification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for computers to check if claims are true or false. Currently, most systems just look at text and don’t need to do complex thinking or combine different types of evidence. The new task is called “multi-hop multimodal claim verification” and it challenges computers to think about multiple pieces of evidence (like text, images, and tables) and decide if they support or reject a claim. To study this, the researchers created a big dataset with 15,000 claims and paired each one with different types of evidence. They found that even the best computer systems struggle with this task, especially when it gets harder to think about multiple pieces of evidence. This new way of checking claims could lead to more advanced research in the future. |
