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Large Vision-Language Models: Evaluating MMStar Benchmark
Large vision language models (LVLMs) demonstrate powerful visual perception and understanding capabilities. However, researchers have identified two primary issues: 1) unnecessary visual content for some samples, and 2) unintentional data leakage in LVLM training.
Practical Solutions and Value
To address these challenges, MMStar, an elite vision-indispensable multi-modal benchmark, has been developed by a collaboration of researchers from top institutions. MMStar benchmarks six core capabilities and 18 detailed axes, aiming to evaluate LVLMs’ multi-modal capacities with carefully balanced and purified samples. The benchmark underwent a meticulous data curation process to ensure visual dependency, minimal data leakage, and requirement of advanced multi-modal capabilities for resolution. This process involved automated pipeline filtering and manual review conducted by experts.
Two unique metrics were proposed to measure data leakage and actual performance gain from the multi-modal training process. The MMStar benchmark was then used to evaluate 16 diverse LVLMs, with even the best model scoring under 60 on average, highlighting the practical value of this benchmark in identifying the strengths and weaknesses of LVLMs.
For companies looking to evolve with AI, MMStar provides a crucial tool for understanding the capabilities of large vision-language models. Additionally, it is essential to identify automation opportunities, define KPIs, select appropriate AI solutions, and implement gradually. For further insights and practical AI solutions, connect with us at hello@itinai.com or stay tuned on our Telegram channel or Twitter.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Are We on the Right Way for Evaluating Large Vision-Language Models? This AI Paper from China Introduces MMStar: An Elite Vision-Dependent Multi-Modal Benchmark
- MarkTechPost
- Twitter – @itinaicom
