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While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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ALBERT: A lite BERT for self-supervised learning of language representations
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Generating persona consistent dialogues by exploiting natural language inference
Haoyu Song, Wei-Nan Zhang, Jingwen Hu, and Ting Liu. 2020 · 2020
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Can NLI models verify QA systems’ predictions?
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Measuring and improving consistency in pretrained language models
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BeliefBank: Adding memory to a pre-trained language model for a systematic notion of belief
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ViLT: Vision-and-language transformer without convolution or region supervision
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Logic-guided data augmentation and regularization for consistent question answering
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Adversarial NLI: A new benchmark for natural language understanding
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Differentiation of blackbox combinatorial solvers
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Unsupervised commonsense question answering with self-talk
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Improving factual completeness and consistency of image-to-text radiology report generation
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TRUE: Re-evaluating factual consistency evaluation
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Maieutic prompting: Logically consistent reasoning with recursive explanations
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SummaC: Re-visiting NLI-based models for inconsistency detection in summarization
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