---
title: "Who cites Questions Are All You Need to — 36 citing papers"
description: "36 papers of the Elamur Research library cite «Questions Are All You Need to Train a Dense Passage Retriever», found in their…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Questions Are All You Need to Train a Dense…](https://md.research.elamur.ai/free/en/papers/sachan-2023-questions-are-all-you-need/) / cited by

# Who cites «Questions Are All You Need to Train a Dense Passage Retriever»

36 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Questions Are All You Need to Train a Dense Passage Retriever»](https://md.research.elamur.ai/free/en/papers/sachan-2023-questions-are-all-you-need/)

1. **[Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://md.research.elamur.ai/free/en/papers/sun-2023-chatgpt-good-search-investigating-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weiwei Sun, Lingyong Yan, Xinyu Ma, et al.*2023 · 496 citations*
2. [**REPLUG: Retrieval-Augmented Black-Box Language Models arXiv:2301.12652**Weijia Shi, Sewon Min, Michihiro Yasunaga, et al.*2023 · 492 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/)
3. **[Large Language Models for Information Retrieval: A Survey](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng Dou;, Ji-Rong Wen*2023 · 339 citations*
4. [**Generate rather than Retrieve: Large Language Models are Strong Context Generators arXiv:2209.10063**Wenhao Yu, Dan Iter, Shuohang Wang, et al.*2022 · 281 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/)
5. [**RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval arXiv:2401.18059**Parth Sarthi, Salman Abdullah, Aditi Tuli, et al.*2024 · 193 citations*](https://md.research.elamur.ai/free/en/papers/sarthi-2024-raptor-recursive-abstractive-processing-tree/)
6. **[Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/yu-2024-chain-note-enhancing-robustness-retrieval/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al.*2024 · 149 citations*
7. [**RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models arXiv:2309.15088**Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/pradeep-2023-rankvicuna-zero-shot-listwise-document/)
8. [**Gecko: Versatile Text Embeddings Distilled from Large Language Models arXiv:2403.20327**Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-gecko-versatile-text-embeddings-distilled/)
9. [**Improving Language Models via Plug-and-Play Retrieval Feedback arXiv:2305.14002**Wenhao Yu, Zhihan Zhang, Zhenwen Liang, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-improving-language-models-plug-play/)
10. [**IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions**Wenhao Yu, Meng Jiang, Peter Clark, et al.*2023 · 26 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/sachan-2023-questions-are-all-you-need/) — Questions Are All You Need to Train a Dense Passage Retriever
