OFA-X

Reported on 6 benchmarks across 3 tasks · 1 paper · 3 SOTA

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

Natural Language Processing6 results

Natural Language Inferenceone-SNLI-VE
Accuracy· 2022-12-08
80.9
SOTA
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations arXiv:2212.04231
Explanation GenerationonVQA-X
Human Explanation Rating· 2022-12-08
89.5
SOTA
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations arXiv:2212.04231
Explanation Generationone-SNLI-VE
Human Explanation Rating· 2022-12-08
85.7
SOTA
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations arXiv:2212.04231
Visual Question Answering (VQA)onVQA-X
Accuracy· 2022-12-08
91.2
best: 92.6 (OFA-X-MT)
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations arXiv:2212.04231
Visual Question Answering (VQA)onVCR (Q-A) test
Accuracy· 2022-12-08
71.2
best: 89.4 (GPT4RoI)
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations arXiv:2212.04231
Explanation GenerationonVCR
Human Explanation Rating· 2022-12-08
68.9
best: 77.3 (OFA-X-MT)
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations arXiv:2212.04231