🔗 Who cites «Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks»

31 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · ← back to «Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks»

1. Jamba: A Hybrid Transformer-Mamba Language ModelarXiv:2403.19887Opher Lieber, Barak Lenz, Hofit Bata, et al.2024 · 159 citations
2. A Survey on Efficient Inference for Large Language ModelsarXiv:2404.14294Zixuan Zhou, Xuefei Ning, Ke Hong, et al.2024 · 113 citations
3. An Empirical Study of Mamba-based Language ModelsarXiv:2406.07887Roger Waleffe, Wonmin Byeon, Duncan Riach, et al.2024 · 67 citations
4. A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
5. Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language ModelingarXiv:2406.07522Liliang Ren, Yang Liu, Yadong Lu, et al.2024 · 59 citations
6. 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
7. Hymba: A Hybrid-head Architecture for Small Language ModelsarXiv:2411.13676Xin Dong, Yonggan Fu, Shizhe Diao, et al.2024 · 50 citations
8. Zamba: A Compact 7B SSM Hybrid ModelarXiv:2405.16712Paolo Glorioso, Quentin Anthony, Yury Tokpanov, et al.2024 · 50 citations
9. The Hidden Attention of Mamba ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ameen Ali Ali, Itamar Zimerman, Lior Wolf2025 · 32 citations
10. RNNs are not Transformers (Yet): The Key Bottleneck on In-context RetrievalarXiv:2402.18510Kaiyue Wen, Xingyu Dang, Kaifeng Lyu2024 · 17 citations
11. Task-Related In-Context LearningFindings of the Association for Computational LinguisticsWenqiang Wang, Peng Chen, Yan Xiao, et al.2026
12. Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid ArchitecturesFindings of the Association for Computational LinguisticsShenran Wang, Timothy Tin-Long Tse, Jian Zhu2026
13. XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake DetectionFindings of the Association for Computational LinguisticsKwok-Ho Ng, Tingting Song, Yongdong WU, et al.2026

18 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/park-2024-mamba-learn-learn-comparative-study/cited-by/