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Can NLI Models Verify QA Systems' Predictions?
[article]
2021
arXiv
pre-print
To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just "good enough" in the context of imperfect QA datasets. We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (document context) to contain all necessary information to support the hypothesis (proposed answer to the question). We leverage large pre-trained models and recent prior datasets to
arXiv:2104.08731v2
fatcat:dw5m5vg7ebh2poidozbgtwrabi