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dc.contributor.authorKejriwal, Mayank
dc.contributor.authorSantos, Henrique
dc.contributor.authorMulvehill, Alice
dc.contributor.authorMcGuinness, Deborah
dc.date.accessioned2022-04-25T13:07:59Z
dc.date.available2022-04-25T13:07:59Z
dc.date.issued2022-04-22
dc.identifier.citationKejriwal, M., Santos, H., Mulvehill, A.M. et al. Designing a strong test for measuring true common-sense reasoning. Nat Mach Intell 4, 318–322 (2022).en_US
dc.identifier.otherhttps://doi.org/10.1038/s42256-022-00478-4
dc.identifier.urihttps://rdcu.be/cLUHR
dc.identifier.urihttps://hdl.handle.net/20.500.13015/4979
dc.description.abstractCommon-sense reasoning has recently emerged as an important test for artificial general intelligence, especially given the much-publicized successes of language representation models such as T5, BERT and GPT-3. Currently, typical benchmarks involve question answering tasks, but to test the full complexity of common-sense reasoning, more comprehensive evaluation methods that are grounded in theory should be developed.
dc.language.isoen_USen_US
dc.publisherNature Machine Intelligenceen_US
dc.subjectCommon-sense reasoningen_US
dc.subjectartificial intelligenceen_US
dc.subject.othercommon-sense reasoning
dc.subject.otherartificial intelligence
dc.titleDesigning a strong test for measuring true common-sense reasoningen_US
dc.typeArticleen_US
dc.rights.holderThis Item is protected by copyright and/or related rights. 


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