Papers by Nadine El-Naggar
Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages (2025.emnlp-main)
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| Challenge: | Whether language models have inductive biases favoring typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using artificial languages (ALs). |
| Approach: | They extend their context-free AL formalization by adopting Generalized Categorial Grammar (GCG) . they also examine the generalization ability of LMs to process unseen longer test sentences . |
| Outcome: | The proposed models better capture features of natural languages and can process unseen longer test sentences. |
Theoretical Conditions and Empirical Failure of Bracket Counting on Long Sequences with Linear Recurrent Networks (2023.eacl-srw)
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| Challenge: | Existing studies have shown that linear RNNs with unbounded activation functions are difficult to train effectively and do not learn exact counting behaviour. |
| Approach: | They propose to identify the necessary conditions for a linear single-cell RNN to have the ability to count and to investigate how these conditions relate to the empirical behaviour of trained linear RNN models. |
| Outcome: | The proposed model is a linear single-cell RNN with an unbounded activation function and a Dyck-1-like balanced bracket language. |