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On the Computational Power of Transformers and Its Implications in Sequence Modeling.

, , and . CoNLL, page 455-475. Association for Computational Linguistics, (2020)

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Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions., , , and . ICLR, OpenReview.net, (2024)Evaluating In-Context Learning of Libraries for Code Generation., , , and . NAACL-HLT, page 2908-2926. Association for Computational Linguistics, (2024)On the Computational Power of Transformers and Its Implications in Sequence Modeling., , and . CoNLL, page 455-475. Association for Computational Linguistics, (2020)Evaluating In-Context Learning of Libraries for Code Generation., , , and . CoRR, (2023)Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions., , , and . ACL (1), page 5767-5791. Association for Computational Linguistics, (2023)When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks., , and . EMNLP, page 648-669. Association for Computational Linguistics, (2022)Universal Adversarial Triggers Are Not Universal., , and . CoRR, (2024)Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions., , , and . CoRR, (2023)Are NLP Models really able to Solve Simple Math Word Problems?, , and . NAACL-HLT, page 2080-2094. Association for Computational Linguistics, (2021)Revisiting the Compositional Generalization Abilities of Neural Sequence Models., , , and . ACL (2), page 424-434. Association for Computational Linguistics, (2022)