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The TRUE framework provides a new or improved methodology for evaluating factual consistency in natural language processing. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 1, 2022Open Access

TRUE: Re-evaluating Factual Consistency Evaluation

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Abstract

Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, Yossi Matias. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

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Honovich et al. (2022) studied this question.

synapsesocial.com/papers/6a087fd1ab15ea61dee8e37c https://doi.org/10.18653/v1/2022.naacl-main.287

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TRUE: Re-evaluating Factual Consistency Evaluation

Abstract

Discussion

Cite This Study

TRUE: Re-evaluating Factual Consistency Evaluation

Abstract

Discussion

Cite This Study

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