🔗 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»

1. A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language ModelsACM 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 Li2024 · 1,010 citations
2. Active Retrieval Augmented GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Zhengbao Jiang, Frank Xu, Luyu Gao, et al.2023 · 872 citations
3. Unsupervised Dense Information Retrieval with Contrastive LearningarXiv:2112.09118Gautier Izacard, Mathilde Caron, Lucas Hosseini, et al.2021 · 836 citations
4. Atlas: Few-shot Learning with Retrieval Augmented Language ModelsarXiv:2208.03299Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.2022 · 673 citations
5. Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question AnsweringTransactions of the Association for Computational Linguistics (TACL)Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al.2023 · 403 citations
6. Query Rewriting in Retrieval-Augmented Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Xinbei Ma, Yeyun Gong, Pengcheng He, et al.2023 · 384 citations
7. Generate rather than Retrieve: Large Language Models are Strong Context GeneratorsarXiv:2209.10063Wenhao Yu, Dan Iter, Shuohang Wang, et al.2022 · 281 citations
8. Evaluating Open-Domain Question Answering in the Era of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (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 answeringarXiv:2203.05115Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.2022 · 209 citations
10. Improving Passage Retrieval with Zero-Shot Question GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Devendra Sachan, Mike Lewis, Mandar Joshi, et al.2022 · 199 citations
11. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityarXiv:2310.07521Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.2023 · 171 citations
12. Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al.2024 · 149 citations
13. A Survey on Recent Advances in LLM-Based Multi-turn Dialogue SystemsACM Computing SurveysZihao Yi, Jiarui Ouyang, Zhe Xu, et al.2024 · 130 citations
14. SlideVQA: A Dataset for Document Visual Question Answering on Multiple ImagesProceedings of the AAAI Conference on Artificial IntelligenceRyota Tanaka, Kyosuke Nishida, Kosuke Nishida, et al.2023 · 116 citations
15. Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting EvidenceConference on Empirical Methods in Natural Language Processing (EMNLP)Hung-Ting Chen, Michael Zhang, Eunsol Choi2022 · 105 citations
16. Retrieval-Augmented Generation for Natural Language Processing: A SurveyArtificial Intelligence ReviewShangyu Wu, Ying Xiong, Yufei Cui, et al.2024 · 86 citations
17. Questions Are All You Need to Train a Dense Passage RetrieverTransactions of the Association for Computational Linguistics (TACL)Devendra Singh Sachan, Mike Lewis, Dani Yogatama, et al.2023 · 65 citations
18. Improving Language Models via Plug-and-Play Retrieval FeedbackarXiv:2305.14002Wenhao Yu, Zhihan Zhang, Zhenwen Liang, et al.2023 · 54 citations
19. RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoxi Li, Jiajie Jin, Yujia Zhou, et al.2025 · 19 citations
20. R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement LearningFindings of the Association for Computational LinguisticsQingfei Zhao, Ruobing Wang, Dingling Xu, et al.2026 · 7 citations
21. R^3AG: Retriever Routing for Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Tong Zhao, Yutao Zhu, Yucheng Tian, et al.2026

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