Piecing It All Together: Verifying Multi-Hop Multimodal Claims

• URL: http://arxiv.org/abs/2411.09547v1
• Date: Thu, 14 Nov 2024 16:01:33 GMT
• Title: Piecing It All Together: Verifying Multi-Hop Multimodal Claims
• Authors: Haoran Wang, Aman Rangapur, Xiongxiao Xu, Yueqing Liang, Haroon Gharwi, Carl Yang, Kai Shu,
• Abstract summary: 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. We construct MMCV, a large-scale dataset comprising 16k multi-hop claims paired with multimodal evidence, with additional input from human feedback.
• Score: 39.68850054331197
• License:
• 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.

Related papers

• Multi-modal Retrieval Augmented Multi-modal Generation: A Benchmark, Evaluate Metrics and Strong Baselines [63.427721165404634]
  This paper investigates an intriguing task of Multi-modal Retrieval Augmented Multi-modal Generation (M$2$RAG) This task requires foundation models to browse multi-modal web pages, with mixed text and images, and generate multi-modal responses for solving user queries. We construct a benchmark for M$2$RAG task, equipped with a suite of text-modal metrics and multi-modal metrics to analyze the capabilities of existing foundation models.
  arXiv  Detail & Related papers  (2024-11-25T13:20:19Z)
• MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models [76.1999277491816]
  Multimodal Multi-image Understanding (MMIU) is a comprehensive evaluation suite designed to assess Large Vision-Language Models (LVLMs) MMIU encompasses 7 types of multi-image relationships, 52 tasks, 77K images, and 11K meticulously curated multiple-choice questions. Our evaluation of 24 popular LVLMs, including both open-source and proprietary models, reveals significant challenges in multi-image comprehension.
  arXiv  Detail & Related papers  (2024-08-05T17:56:41Z)
• MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking [0.283600654802951]
  We present a summarization model designed to generate claim-specific summaries useful for fact-checking from multimodal datasets. We introduce a dynamic perceiver-based model that can handle inputs from multiple modalities of arbitrary lengths. Our approach outperforms the SOTA approach by 4.6% in the claim verification task on the MOCHEG dataset.
  arXiv  Detail & Related papers  (2024-07-18T01:33:20Z)
• Needle In A Multimodal Haystack [79.81804334634408]
  We present the first benchmark specifically designed to evaluate the capability of existing MLLMs to comprehend long multimodal documents. Our benchmark includes three types of evaluation tasks: multimodal retrieval, counting, and reasoning. We observe that existing models still have significant room for improvement on these tasks, especially on vision-centric evaluation.
  arXiv  Detail & Related papers  (2024-06-11T13:09:16Z)
• Generative Multimodal Models are In-Context Learners [60.50927925426832]
  We introduce Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences. Emu2 exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning.
  arXiv  Detail & Related papers  (2023-12-20T18:59:58Z)
• Read, Look or Listen? What's Needed for Solving a Multimodal Dataset [7.0430001782867]
  We propose a two-step method to analyze multimodal datasets, which leverages a small seed of human annotation to map each multimodal instance to the modalities required to process it. We apply our approach to TVQA, a video question-answering dataset, and discover that most questions can be answered using a single modality, without a substantial bias towards any specific modality. We analyze the MERLOT Reserve, finding that it struggles with image-based questions compared to text and audio, but also with auditory speaker identification.
  arXiv  Detail & Related papers  (2023-07-06T08:02:45Z)
• How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension [64.76737510530184]
  We design a taxonomy to categorize commonly-seen multi-answer MRC instances. We analyze how well different paradigms of current multi-answer MRC models deal with different types of multi-answer instances.
  arXiv  Detail & Related papers  (2023-06-01T08:22:21Z)
• Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions [27.983902791798965]
  We develop a model that generates dilution text that maintains relevance and topical coherence with the image and existing text. We find that the performance of task-specific fusion-based multimodal classifiers drops by 23.3% and 22.5%, respectively, in the presence of dilutions generated by our model. Our work aims to highlight and encourage further research on the robustness of deep multimodal models to realistic variations.
  arXiv  Detail & Related papers  (2022-11-04T17:58:02Z)
• Logically at the Factify 2022: Multimodal Fact Verification [2.8914815569249823]
  This paper describes our participant system for the multi-modal fact verification (Factify) challenge at AAAI 2022. Two baseline approaches are proposed and explored including an ensemble model and a multi-modal attention network. Our best model is ranked first in leaderboard which obtains a weighted average F-measure of 0.77 on both validation and test set.
  arXiv  Detail & Related papers  (2021-12-16T23:34:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.