BLINK ↗41.3%
LLaVA-v1.5-13B-xtuner · BLINK · Test accuracy · 1,907 test items ↗
Reported model / MODEL EVIDENCE
LLaVA-v1.5-13B-xtuner
2 benchmarks · 2 observations · 2 sources
Scores at a glance
2 benchmarks · large dot: LLaVA-v1.5-13B-xtuner; small dots: closest models where reported
LLavA-v1.5-13B-xtuner · MMT-Bench & MuirBench · MMT-Bench accuracy · primary leaderboard ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
2 of 2 shared benchmark comparisons
2 of 2 shared benchmark comparisons
How similarity is measured
Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI — published result table ↗
All 22 authors
Kaining Ying, Fanqing Meng, Jin Wang, Zhiqian Li, Han Lin, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, Jiayi Lei, Quanfeng Lu, Runjian Chen, Peng Xu, Renrui Zhang, Haozhe Zhang, Peng Gao, Yali Wang, Yu Qiao, Ping Luo, Kaipeng Zhang, Wenqi Shao
- BLINK: Multimodal Large Language Models Can See but Not Perceive — published result table ↗
All 10 authors
Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei-Chiu Ma, Ranjay Krishna
Names used in source tables
LLaVA-v1.5-13B-xtuner · LLavA-v1.5-13B-xtuner