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BECEL: Benchmark for Consistency Evaluation of Language Models.

, , and . COLING, page 3680-3696. International Committee on Computational Linguistics, (2022)

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Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning., , and . CoRR, (2018)KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations., , , , and . ACL (2), page 540-553. Association for Computational Linguistics, (2023)BECEL: Benchmark for Consistency Evaluation of Language Models., , and . COLING, page 3680-3696. International Committee on Computational Linguistics, (2022)Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence., , and . NAACL-HLT (Findings), page 2030-2042. Association for Computational Linguistics, (2022)Recurrent neural network-based semantic variational autoencoder for Sequence-to-sequence learning., , and . Inf. Sci., (2019)Corrigendum to "Recurrent neural network-based semantic variational autoencoder for Sequence-to-sequence learning" Information Sciences 490 (2019) 59-73., , and . Inf. Sci., (2020)KoBEST: Korean Balanced Evaluation of Significant Tasks., , , and . COLING, page 3697-3708. International Committee on Computational Linguistics, (2022)Improving Language Models' Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary., and . EMNLP, page 8496-8510. Association for Computational Linguistics, (2023)Consistency Analysis of ChatGPT., and . EMNLP, page 15970-15985. Association for Computational Linguistics, (2023)