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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 BASE Show 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; Titov, Ivan. - : arXiv, 2021 Abstract: There has been a lot of interest in understanding what information is captured by hidden representations of language models (LMs). Typically, interpretation methods i) do not guarantee that the model actually uses the encoded information, and ii) do not discover small subsets of neurons responsible for a considered phenomenon. Inspired by causal mediation analysis, we propose a method that discovers within a neural LM a small subset of neurons responsible for a particular linguistic phenomenon, i.e., subsets causing a change in the corresponding token emission probabilities. We use a differentiable relaxation to approximately search through the combinatorial space. An $L_0$ regularization term ensures that the search converges to discrete and sparse solutions. We apply our method to analyze subject-verb number agreement and gender bias detection in LSTMs. We observe that it is fast and finds better solutions than the alternative (REINFORCE). Our experiments confirm that each of these phenomenons is mediated ... : 12 pages, 4 figures, 6 tables ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2112.06837 https://dx.doi.org/10.48550/arxiv.2112.06837 BASE Hide 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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