🔗 Who cites «Transformers Can Achieve Length Generalization But Not Robustly»

25 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · ← back to «Transformers Can Achieve Length Generalization But Not Robustly»

1. Premise Order Matters in Reasoning with Large Language ModelsarXiv:2402.08939Xinyun Chen, Ryan A. Chi, Xuezhi Wang, et al.2024 · 58 citations
2. 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
3. Dataset Decomposition: Faster LLM Training with Variable Sequence Length CurriculumConference on Neural Information Processing Systems (NeurIPS)Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, et al.2024 · 16 citations
4. Looped Transformers for Length GeneralizationarXiv:2409.15647Ying Fan, Yilun Du, Kannan Ramchandran, et al.2024 · 13 citations
5. Position Encoding with Random Float Sampling Enhances Length Generalization of TransformersFindings of the Association for Computational LinguisticsAtsushi Shimizu, Shohei Taniguchi, Yutaka Matsuo2026

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

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