Summary of Reefknot: a Comprehensive Benchmark For Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models, by Kening Zheng et al.
Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models
by Kening Zheng, Junkai Chen, Yibo Yan, Xin Zou, Xuming Hu
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper addresses the issue of relation hallucinations in multimodal large language models (MLLMs), which have been largely neglected despite their complexity and importance. The existing benchmarks and datasets for relation hallucinations lack detailed evaluation and effective mitigation, leading to biased results. To address this challenge, the authors introduce Reefknot, a comprehensive benchmark containing over 20,000 real-world samples that targets relation hallucinations. They also provide a systematic definition of relation hallucinations, integrating perceptive and cognitive perspectives, and construct a relation-based corpus using the Visual Genome scene graph dataset. The comparative evaluation reveals significant limitations in current MLLMs’ ability to handle relation hallucinations, highlighting the need for more advanced reasoning capabilities. Additionally, the authors propose a novel confidence-based mitigation strategy that reduces the hallucination rate by an average of 9.75% across three datasets, including Reefknot. This work offers valuable insights for achieving trustworthy multimodal intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Relation hallucinations in large language models are like “seeing” things that aren’t really there! This problem has been mostly ignored, even though it’s very important. The existing tests and datasets for this issue don’t give us a clear picture of how well the models are doing. To fix this, researchers created a new test called Reefknot with over 20,000 real-life examples that challenges large language models to reason about relationships between objects. They also came up with a definition of relation hallucinations and built a special dataset using pictures from the internet. The results show that current large language models aren’t very good at handling this problem. To fix this, they suggest a new way to reduce mistakes by 9.75% on average across three datasets. This is important because we want our language models to be reliable! |
Keywords
* Artificial intelligence * Hallucination
