Evaluating the Quality of Hallucination Benchmarks for Large Vision-Language Models
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What problem does the paper attempt to solve? Is this a new problem?
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What scientific hypothesis does this paper seek to validate?
This paper aims to validate the hypothesis related to the quality of hallucination benchmarks for Large Vision-Language Models (LVLMs) . The study focuses on assessing the reliability and validity of existing hallucination benchmarks separately through various indicators . The hypothesis seeks to evaluate the degree of hallucination in LVLMs by examining the quality of existing benchmarks, identifying problems such as inconsistent evaluation results and misalignment with human evaluation, and proposing a framework for measuring the quality of hallucination benchmarks .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
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Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
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How were the experiments in the paper designed?
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What is the dataset used for quantitative evaluation? Is the code open source?
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Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified regarding the evaluation of hallucination benchmarks for Large Vision-Language Models (LVLMs) . The paper introduces a Hallucination benchmark Quality Measurement framework (HQM) that assesses the reliability and validity of existing hallucination benchmarks . Through the HQM framework, the paper evaluates the quality of both the High-Quality Hallucination Benchmark (HQH) and existing benchmarks, demonstrating that HQH exhibits the highest reliability and comparable validity to close-ended tasks, ensuring credible and meaningful hallucination evaluation for LVLMs .
Furthermore, the paper conducts extensive evaluations on over 10 representative LVLMs, including GPT-4o and Gemini-Vision-Pro, to provide an in-depth analysis of hallucination issues in existing models . The results of the evaluations reveal that while some models perform better than others, more than half of the models exhibit a hallucination rate exceeding 40%, indicating significant room for improvement in mitigating hallucination in LVLMs . Additionally, the analysis suggests that models with larger parameter sizes tend to have fewer hallucination issues, implying that parameter size may play a role in addressing the hallucination problem .
In conclusion, the experiments and results in the paper offer robust support for the scientific hypotheses related to evaluating hallucination benchmarks for LVLMs. The comprehensive evaluations, framework, and analysis provided contribute significantly to understanding and addressing the issue of hallucination in Large Vision-Language Models .
What are the contributions of this paper?
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###HQM Framework