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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Towards Interactive Language Modeling ... ter Hoeve, Maartje; Kharitonov, Evgeny; Hupkes, Dieuwke. - : arXiv, 2021 BASE Show details 2 Language Models Use Monotonicity to Assess NPI Licensing ... Jumelet, Jaap; Denić, Milica; Szymanik, Jakub. - : arXiv, 2021 BASE Show details 3 Language Modelling as a Multi-Task Problem ... Weber, Lucas; Jumelet, Jaap; Bruni, Elia. - : arXiv, 2021 BASE Show details 4 How BPE Affects Memorization in Transformers ... Kharitonov, Eugene; Baroni, Marco; Hupkes, Dieuwke. - : arXiv, 2021 Abstract: Training data memorization in NLP can both be beneficial (e.g., closed-book QA) and undesirable (personal data extraction). In any case, successful model training requires a non-trivial amount of memorization to store word spellings, various linguistic idiosyncrasies and common knowledge. However, little is known about what affects the memorization behavior of NLP models, as the field tends to focus on the equally important question of generalization. In this work, we demonstrate that the size of the subword vocabulary learned by Byte-Pair Encoding (BPE) greatly affects both ability and tendency of standard Transformer models to memorize training data, even when we control for the number of learned parameters. We find that with a large subword vocabulary size, Transformer models fit random mappings more easily and are more vulnerable to membership inference attacks. Similarly, given a prompt, Transformer-based language models with large subword vocabularies reproduce the training data more often. We ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2110.02782 https://dx.doi.org/10.48550/arxiv.2110.02782 BASE Hide details 5 Causal Transformers Perform Below Chance on Recursive Nested Constructions, Unlike Humans ... Lakretz, Yair; Desbordes, Théo; Hupkes, Dieuwke. - : arXiv, 2021 BASE Show details 6 Sparse Interventions in Language Models with Differentiable Masking ... De Cao, Nicola; Schmid, Leon; Hupkes, Dieuwke. - : arXiv, 2021 BASE Show details 7 Language Models Use Monotonicity to Assess NPI Licensing ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Dieuwke; ., Jakub. - : Underline Science Inc., 2021 BASE Show details 8 Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Dieuwke; ., Kate. - : Underline Science Inc., 2021 BASE Show details 9 Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Dieuwke; Jia, Robin. - : Underline Science Inc., 2021 BASE Show details

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