🔗 Who cites «How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval»

67 papers of our library cite it (of the 141,840 whose bibliographies we read) · 4 with a page here, the most cited first · ← back to «How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval»

1. RA-DIT: Retrieval-Augmented Dual Instruction TuningXi Victoria Lin, Xilun Chen, Mingda Chen, et al.2023 · 145 citations
2. Dense X Retrieval: What Retrieval Granularity Should We Use?Tong Chen, Hongwei Wang, Sihao Chen, et al.2024 · 142 citations
3. Retrieval meets Long Context Large Language ModelsPeng Xu, Wei Ping, Xianchao Wu, et al.2023 · 129 citations
4. InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context MemoryChaojun Xiao, Pengle Zhang, Xu Han, et al.2024 · 125 citations

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

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