---
title: "Who cites How to Train Your Dragon: — 67 citing papers"
description: "67 papers of the Elamur Research library cite «How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval»…"
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---
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# 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»](https://md.research.elamur.ai/free/en/papers/lin-2023-how-train-your-dragon-diverse/)

1. [**RA-DIT: Retrieval-Augmented Dual Instruction Tuning arXiv:2310.01352**Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/) **[Dense X Retrieval: What Retrieval Granularity Should We Use?](https://md.research.elamur.ai/free/en/papers/chen-2024-dense-x-retrieval-what-retrieval/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tong Chen, Hongwei Wang, Sihao Chen, et al.*2024 · 142 citations* [**Retrieval meets Long Context Large Language Models arXiv:2310.03025**Peng Xu, Wei Ping, Xianchao Wu, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-retrieval-meets-long-context-large/) **[InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory](https://md.research.elamur.ai/free/en/papers/xiao-2024-infllm-training-free-long-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Chaojun 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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