Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2408.09429v1
• Date: Sun, 18 Aug 2024 10:07:02 GMT
• Title: Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models
• Authors: Kening Zheng, Junkai Chen, Yibo Yan, Xin Zou, Xuming Hu,
• Abstract summary: Hallucination issues persistently plagued current multimodal large language models (MLLMs) We introduce Reefknot, a benchmark specifically targeting relation hallucinations, consisting of over 20,000 samples derived from real-world scenarios. Our comparative evaluation across three distinct tasks revealed a substantial shortcoming in the capabilities of current MLLMs to mitigate relation hallucinations.
• Score: 13.48296910438554
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Hallucination issues persistently plagued current multimodal large language models (MLLMs). While existing research primarily focuses on object-level or attribute-level hallucinations, sidelining the more sophisticated relation hallucinations that necessitate advanced reasoning abilities from MLLMs. Besides, recent benchmarks regarding relation hallucinations lack in-depth evaluation and effective mitigation. Moreover, their datasets are typically derived from a systematic annotation process, which could introduce inherent biases due to the predefined process. To handle the aforementioned challenges, we introduce Reefknot, a comprehensive benchmark specifically targeting relation hallucinations, consisting of over 20,000 samples derived from real-world scenarios. Specifically, we first provide a systematic definition of relation hallucinations, integrating perspectives from perceptive and cognitive domains. Furthermore, we construct the relation-based corpus utilizing the representative scene graph dataset Visual Genome (VG), from which semantic triplets follow real-world distributions. Our comparative evaluation across three distinct tasks revealed a substantial shortcoming in the capabilities of current MLLMs to mitigate relation hallucinations. Finally, we advance a novel confidence-based mitigation strategy tailored to tackle the relation hallucinations problem. Across three datasets, including Reefknot, we observed an average reduction of 9.75% in the hallucination rate. We believe our paper sheds valuable insights into achieving trustworthy multimodal intelligence. Our dataset and code will be released upon paper acceptance.

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