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Models/Prompt Tuning

Prompt Tuning

Reported on 7 benchmarks across 3 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing7 results

Visual Question Answering (VQA)onVQA v2 test-std
overall· 2022-08-04
78.53
best: 84.03 (BEiT-3)
Prompt Tuning for Generative Multimodal Pretrained Models arXiv:2208.02532
Natural Language InferenceonSNLI-VE val
Accuracy· 2022-08-04
90.04
best: 91 (OFA)
Prompt Tuning for Generative Multimodal Pretrained Models arXiv:2208.02532
Natural Language InferenceonSNLI-VE test
Accuracy· 2022-08-04
90.12
best: 91.2 (OFA)
Prompt Tuning for Generative Multimodal Pretrained Models arXiv:2208.02532
Image CaptioningonCOCO Captions
BLEU-4· 2022-08-04
41.81
best: 46.5 (mPLUG)
Prompt Tuning for Generative Multimodal Pretrained Models arXiv:2208.02532
Image CaptioningonCOCO Captions
CIDER· 2022-08-04
141.4
best: 155.1 (mPLUG)
Prompt Tuning for Generative Multimodal Pretrained Models arXiv:2208.02532
Image CaptioningonCOCO Captions
METEOR· 2022-08-04
31.51
best: 33.9 (CoCa)
Prompt Tuning for Generative Multimodal Pretrained Models arXiv:2208.02532
Image CaptioningonCOCO Captions
SPICE· 2022-08-04
24.42
best: 27 (VAST)
Prompt Tuning for Generative Multimodal Pretrained Models arXiv:2208.02532