🔗 Who cites «How to Train Long-Context Language Models (Effectively)»

78 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «How to Train Long-Context Language Models (Effectively)»

1. Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and InferencearXiv:2412.13663Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.2024 · 355 citations
2. SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language ModelarXiv:2502.02737Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.2025 · 171 citations
3. LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context MultitasksarXiv:2412.15204Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.2024 · 130 citations
4. Olmo 3arXiv:2512.13961Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.2025 · 109 citations
5. MagicPIG: LSH Sampling for Efficient LLM GenerationarXiv:2410.16179Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, et al.2024 · 66 citations
6. A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
7. mmBERT: A Modern Multilingual Encoder with Annealed Language LearningarXiv:2509.06888Marc Marone, Orion Weller, William Fleshman, et al.2025 · 59 citations
   New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec ModelsLanguage Resources and Evaluation Conference (LREC)Julia Wunderle, Anton Ehrmanntraut, Jan Pfister, et al.2026 · 7 citations

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

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