ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2409.09318v1
• Date: Sat, 14 Sep 2024 05:31:29 GMT
• Title: ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models
• Authors: Yahan Tu, Rui Hu, Jitao Sang,
• Abstract summary: This paper introduces ODE, an open-set, dynamic protocol for evaluating object existence hallucinations in large language models (MLLMs) Our framework employs graph structures to model associations between real-word concepts and generates novel samples for both general and domain-specific scenarios. Experimental results show that MLLMs exhibit higher hallucination rates with ODE-generated samples, effectively avoiding data contamination.
• Score: 15.156359255401812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hallucination poses a significant challenge for multimodal large language models (MLLMs). However, existing benchmarks for evaluating hallucinations are static, which can lead to potential data contamination. This paper introduces ODE, an open-set, dynamic protocol for evaluating object existence hallucinations in MLLMs. Our framework employs graph structures to model associations between real-word concepts and generates novel samples for both general and domain-specific scenarios. The dynamic combination of concepts, along with various combination principles, ensures a broad sample distribution. Experimental results show that MLLMs exhibit higher hallucination rates with ODE-generated samples, effectively avoiding data contamination. Moreover, these samples can also be used for fine-tuning to improve MLLM performance on existing benchmarks.

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