--- title: "Who cites End-to-End Training of — 80 citing papers" description: "80 papers of the Elamur Research library cite «End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question…" canonical: "https://research.elamur.ai/free/en/papers/sachan-2021-end-end-training-multi-document/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sachan-2021-end-end-training-multi-document/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [End-to-End Training of Multi-Document Reader and…](https://md.research.elamur.ai/free/en/papers/sachan-2021-end-end-training-multi-document/) / cited by # Who cites «End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering» 80 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← back to «End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering»](https://md.research.elamur.ai/free/en/papers/sachan-2021-end-end-training-multi-document/) 1. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,010 citations* 2. **[Active Retrieval Augmented Generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhengbao Jiang, Frank Xu, Luyu Gao, et al.*2023 · 872 citations* 3. [**Unsupervised Dense Information Retrieval with Contrastive Learning arXiv:2112.09118**Gautier Izacard, Mathilde Caron, Lucas Hosseini, et al.*2021 · 836 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/) 4. [**Atlas: Few-shot Learning with Retrieval Augmented Language Models arXiv:2208.03299**Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.*2022 · 673 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/) 5. **[Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al.*2023 · 403 citations* 6. **[Query Rewriting in Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 384 citations* 7. [**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/) 8. **[Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations* 9. [**Internet-augmented language models through few-shot prompting for open-domain question answering arXiv:2203.05115**Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/) 10. **[Improving Passage Retrieval with Zero-Shot Question Generation](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 199 citations* 11. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/) 12. **[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* 13. **[A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://md.research.elamur.ai/free/en/papers/yi-2024-survey-recent-advances-llm-based/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Zihao Yi, Jiarui Ouyang, Zhe Xu, et al.*2024 · 130 citations* 14. **[SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images](https://md.research.elamur.ai/free/en/papers/tanaka-2023-slidevqa-dataset-document-visual-question/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Ryota Tanaka, Kyosuke Nishida, Kosuke Nishida, et al.*2023 · 116 citations* 15. **[Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence](https://md.research.elamur.ai/free/en/papers/chen-2022-rich-knowledge-sources-bring-complex/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hung-Ting Chen, Michael Zhang, Eunsol Choi*2022 · 105 citations* 16. **[Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Shangyu Wu, Ying Xiong, Yufei Cui, et al.*2024 · 86 citations* 17. **[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/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Devendra Singh Sachan, Mike Lewis, Dani Yogatama, et al.*2023 · 65 citations* 18. [**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/) 19. **[RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation](https://md.research.elamur.ai/free/en/papers/li-2025-retrollm-empowering-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoxi Li, Jiajie Jin, Yujia Zhou, et al.*2025 · 19 citations* 20. **[R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zhao-2026-r-search-empowering-llm-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingfei Zhao, Ruobing Wang, Dingling Xu, et al.*2026 · 7 citations* 21. **[R^3AG: Retriever Routing for Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhao-2026-r-textasciicircum3ag-retriever-routing-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tong Zhao, Yutao Zhu, Yucheng Tian, et al.*2026* 59 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/sachan-2021-end-end-training-multi-document/) — End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering