Accuracy: 60.0
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Question Answering
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
논문
P@1: 47.2
Accuracy: 92.4
Accuracy: 71%
Prometheus-2 Answer Correctness: 3.1673Rouge-L: 0.2286AlignScore: 0.1016
Accuracy: 66.5
F1: 32.6
F1: 30.1
Accuracy: 44.0
Prometheus-2 Answer Correctness: 3.4612Rouge-L: 0.2266AlignScore: 0.1224
Prometheus-2 Answer Correctness: 3.0408Rouge-L: 0.2414AlignScore: 0.1378
F1: 80.7
Accuracy: 62.7
Prometheus-2 Answer Correctness: 3.4245Rouge-L: 0.1922AlignScore: 0.0827
Accuracy: 86
Accuracy: 76.1
Accuracy: 52.4