InstructBLIP (GPT Eval)

1 benchmarks · 3 observations · 1 sources

Scores at a glance

1 benchmarks · large dot: InstructBLIP (GPT Eval); small dots: closest models where reported

InstructBLIP (GPT Eval) · HallusionBench · Question accuracy · Official benchmark repository · published comparison ↗

2 more reported results

9.45% · InstructBLIP (GPT Eval) · HallusionBench · Question-pair accuracy · Official benchmark repository · published comparison · HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models ↗

10.11% · InstructBLIP (GPT Eval) · HallusionBench · Figure accuracy · Official benchmark repository · published comparison · HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

LLaVA-1.5 (GPT Eval) →OpenAI

5.6 percentile-point gap

1 of 1 shared benchmark comparisons

ViLT (GPT Eval) →OpenAI

5.6 percentile-point gap

1 of 1 shared benchmark comparisons

LLaVA-1.5 (Human Eval) →

11.1 percentile-point gap

1 of 1 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.

Filter comparisons to this model →

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models — published result table ↗
Guan, Tianrui, Liu, Fuxiao, Wu, Xiyang, Xian, Ruiqi, Li, Zongxia, Liu, Xiaoyu et al. · 2023
All 12 authors
Guan, Tianrui, Liu, Fuxiao, Wu, Xiyang, Xian, Ruiqi, Li, Zongxia, Liu, Xiaoyu, Wang, Xijun, Chen, Lichang, Huang, Furong, Yacoob, Yaser, Manocha, Dinesh, Zhou, Tianyi

Names used in source tables

InstructBLIP (GPT Eval)