🔗 Who cites «Positional Description Matters for Transformers Arithmetic»

27 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «Positional Description Matters for Transformers Arithmetic»

1. On Context Utilization in Summarization with Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mathieu Ravaut, Aixin Sun, Nancy Chen, et al.2024 · 67 citations
2. Repeat After Me: Transformers are Better than State Space Models at CopyingarXiv:2402.01032Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.2024 · 57 citations
3. The pitfalls of next-token predictionarXiv:2403.06963Gregor Bachmann, Vaishnavh Nagarajan2024 · 39 citations
4. Transformers Can Do Arithmetic with the Right EmbeddingsConference on Neural Information Processing Systems (NeurIPS)Sean McLeish, Arpit Bansal, Alex Stein, et al.2024 · 36 citations
5. Transformers Can Achieve Length Generalization But Not RobustlyarXiv:2402.09371Yongchao Zhou, Uri Alon, Xinyun Chen, et al.2024 · 29 citations
6. NumeroLogic: Number Encoding for Enhanced LLMs' Numerical ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Eli Schwartz, Leshem Choshen, Joseph Shtok, et al.2024 · 26 citations
7. Do language models plan ahead for future tokens?arXiv:2404.00859Wilson Wu, John X. Morris, Lionel Levine2024 · 10 citations
8. Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic TasksarXiv:2406.01895Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.2024 · 5 citations

19 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/shen-2023-positional-description-matters-transformers-arithmetic/cited-by/