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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 – 12 of 12 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 Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans ... Lakretz, Yair; Hupkes, Dieuwke; Vergallito, Alessandra. - : arXiv, 2020 BASE Show details 8 Assessing incrementality in sequence-to-sequence models ... Ulmer, Dennis; Hupkes, Dieuwke; Bruni, Elia. - : arXiv, 2019 BASE Show details 9 Compositionality decomposed: how do neural networks generalise? ... Hupkes, Dieuwke; Dankers, Verna; Mul, Mathijs. - : arXiv, 2019 BASE Show details 10 Formal models of structure building in music, language, and animal song Zuidema, Willem; Hupkes, Dieuwke; Wiggins, Geraint A.... In: The origins of musicality (Cambridge, 2018), p. 253-286 MPI für Psycholinguistik Show details 11 Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information ... Giulianelli, Mario; Harding, Jacqueline; Mohnert, Florian. - : arXiv, 2018 BASE Show details 12 Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items ... Jumelet, Jaap; Hupkes, Dieuwke. - : arXiv, 2018 BASE Show details

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