---
title: "Who cites Saku Sugawara — 579 papers of our library"
description: "579 papers of the Elamur Research library cite the works of Saku Sugawara, found in their bibliographies; the most cited first."
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---
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# Who cites Saku Sugawara

583 citations of their works from 579 papers of our library (of the 141,840 whose bibliographies we read) · 41 with a page here, the most cited first · [← Saku Sugawara](https://md.research.elamur.ai/free/en/authors/saku-sugawara/)

1. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,648 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
2. **[MLQA: Evaluating Cross-lingual Extractive Question Answering](https://md.research.elamur.ai/free/en/papers/lewis-2019-mlqa-evaluating-cross-lingual-extractive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Patrick Lewis, Barlas Oğuz, Ruty Rinott, et al.*2019 · 738 citations*
3. **[Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://md.research.elamur.ai/free/en/papers/ho-2020-constructing-multi-hop-qa-dataset/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, et al.*2020 · 735 citations*
4. **[Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://md.research.elamur.ai/free/en/papers/trivedi-2022-interleaving-retrieval-chain-thought-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, et al.*2022 · 711 citations*
5. [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding arXiv:2308.14508**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 506 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/)
6. **[Factual Probing Is MASK: Learning vs. Learning to Recall](https://md.research.elamur.ai/free/en/papers/zhong-2021-factual-probing-is-mask-learning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zexuan Zhong, Dan Friedman, Danqi Chen*2021 · 411 citations*
7. **[Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.*2024 · 381 citations*
8. [**Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy arXiv:2305.15294**Zhihong Shao, Yeyun Gong, Yelong Shen, et al.*2023 · 381 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
9. [**Complexity-Based Prompting for Multi-Step Reasoning arXiv:2210.00720**Yao Fu, Hao Peng, Ashish Sabharwal, et al.*2022 · 343 citations*](https://md.research.elamur.ai/free/en/papers/fu-2022-complexity-based-prompting-multi-step/)
10. **[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 · 335 citations*
11. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning arXiv:2503.09516**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 303 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
12. [**ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning arXiv:2002.04326**Weihao Yu, Zihang Jiang, Yanfei Dong, et al.*2020 · 285 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/)
13. **[HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://md.research.elamur.ai/free/en/papers/gutierrez-2024-hipporag-neurobiologically-inspired-long-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, et al.*2024 · 264 citations*
14. **[Can You Unpack That? Learning to Rewrite Questions-in-Context](https://md.research.elamur.ai/free/en/papers/elgohary-2019-can-you-unpack-that-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ahmed Elgohary, Denis Peskov, Jordan Boyd-Graber*2019 · 245 citations*
15. **[Compositional Questions Do Not Necessitate Multi-hop Reasoning](https://md.research.elamur.ai/free/en/papers/min-2019-compositional-questions-do-not-necessitate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sewon Min, Eric Wallace, Sameer Singh, et al.*2019 · 231 citations*
16. **[Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaoxi Li, Guanting Dong, Jiajie Jin, et al.*2025 · 205 citations*
17. **[Unsupervised Question Answering by Cloze Translation](https://md.research.elamur.ai/free/en/papers/lewis-2019-unsupervised-question-answering-cloze-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Patrick Lewis, Ludovic Denoyer, Sebastian Riedel*2019 · 192 citations*
18. **[Improving Machine Reading Comprehension with General Reading Strategies](https://md.research.elamur.ai/free/en/papers/sun-2019-improving-machine-reading-comprehension-general/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kai Sun, Dian Yu, Dong Yu, et al.*2019 · 172 citations*
19. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
20. **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 162 citations*
21. [**PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling arXiv:2406.02069**Zefan Cai, Yichi Zhang, Bofei Gao, et al.*2024 · 151 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-pyramidkv-dynamic-kv-cache-compression/)
22. **[Understanding Dataset Design Choices for Multi-hop Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2019-understanding-dataset-design-choices-multi/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jifan Chen, Greg Durrett*2019 · 146 citations*
23. **[Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework](https://md.research.elamur.ai/free/en/papers/zhao-2023-verify-edit-knowledge-enhanced-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruochen Zhao, Xingxuan Li, Shafiq Joty, et al.*2023 · 144 citations*
24. [**R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning arXiv:2503.05592**Huatong Song, Jinhao Jiang, Yingqian Min, et al.*2025 · 137 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-r1-searcher-incentivizing-search/)
25. **[DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models](https://md.research.elamur.ai/free/en/papers/su-2024-dragin-dynamic-retrieval-augmented-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weihang Su, Yichen Tang, Qingyao Ai, et al.*2024 · 137 citations*
26. **[Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://md.research.elamur.ai/free/en/papers/shum-2023-automatic-prompt-augmentation-selection-chain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kashun Shum, Shizhe Diao, Tong Zhang*2023 · 128 citations*
27. **[Logic-Guided Data Augmentation and Regularization for Consistent Question Answering](https://md.research.elamur.ai/free/en/papers/asai-2020-logic-guided-data-augmentation-regularization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Akari Asai, Hannaneh Hajishirzi*2020 · 111 citations*
28. **[DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://md.research.elamur.ai/free/en/papers/zheng-2025-deepresearcher-scaling-deep-research-reinforcement/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, et al.*2025 · 108 citations*
29. **[We're Afraid Language Models Aren't Modeling Ambiguity](https://md.research.elamur.ai/free/en/papers/liu-2023-we-re-afraid-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alisa Liu, Zhaofeng Wu, Julian Michael, et al.*2023 · 108 citations*
30. [**MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries arXiv:2401.15391**Yixuan Tang, Yi Yang*2024 · 103 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-multihop-rag-benchmarking-retrieval-augmented/)
31. [**LongAlign: A Recipe for Long Context Alignment of Large Language Models arXiv:2401.18058**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longalign-recipe-long-context/)
32. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
33. **[Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2022-distributed-nli-learning-predict-human/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiang Zhou, Yixin Nie, Mohit Bansal*2022 · 35 citations*
34. **[Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/wang-2022-capture-human-disagreement-distributions-calibrated/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxia Wang, Minghan Wang, Yimeng Chen, et al.*2022 · 15 citations*
35. **[An Annotated Corpus of Reference Resolution for Interpreting Common Grounding](https://md.research.elamur.ai/free/en/papers/udagawa-2019-annotated-corpus-reference-resolution-interpreting/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Takuma Udagawa, Akiko Aizawa*2019 · 14 citations*
36. **[Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization](https://md.research.elamur.ai/free/en/papers/mersinias-2022-mitigating-dataset-artifacts-natural-language/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Michail Mersinias, Panagiotis Valvis*2022 · 3 citations*
37. **[HistoriQA-ThirdRepublic: Multi-Hop Question Answering Corpus for Historical Research, Parliamentary Debates from the French Third Republic (1870-1940)](https://md.research.elamur.ai/free/en/papers/pellet-2026-historiqa-thirdrepublic-multi-hop-question/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aurelien Pellet, Marie Anna Puren, Julien PEREZ*2026 · 2 citations*
38. **[CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps](https://md.research.elamur.ai/free/en/papers/yun-2026-crit-qa-evaluating-multi-hop/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jungmin Yun, June Hyoung Kwon, Youngbin Kim*2026*
39. **[MEUR: A Benchmark for Evaluating Vision-Language Models on Multimodal Event Understanding and Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2026-meur-benchmark-evaluating-vision-language/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Zimu Wang, Yuqi Wang, Tong Chen, et al.*2026*
40. **[Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search](https://md.research.elamur.ai/free/en/papers/mccleary-2026-quantifying-accuracy-cost-impact-design/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kyle A. McCleary, James M. Ghawaly*2026*
41. **[Reasoning Graph-Structured Question Answering: Datasets and Insights from LLM Benchmarking](https://md.research.elamur.ai/free/en/papers/yone-2026-reasoning-graph-structured-question-answering/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Khin Yone, Devasha Trivedi, Anish Pahilajani, et al.*2026*

538 more citing papers of our library have no page here yet.

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