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Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in commercial use cases.
Realm: retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Earlier work this paper cites.
Knowledge graphs
Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D’amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann. 2021 · 2021
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021 · 2021
Earlier work this paper cites.
Generative entity-to-entity stance detection with knowledge graph augmentation
Xinliang Frederick Zhang, Nick Beauchamp, and Lu Wang. 2022 · 2022
Earlier work this paper cites.
Self-rag: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023 · 2023
Earlier work this paper cites.
A unified temporal knowledge graph reasoning model towards interpolation and extrapolation
Kai Chen, Ye Wang, Yitong Li, Aiping Li, Han Yu, and Xin Song. 2024 · 2024
Earlier work this paper cites.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024 · 2024
Cited alongside, same era.
Construction of knowledge graphs: Current state and challenges
Marvin Hofer, Daniel Obraczka, Alieh Saeedi, Hanna Köpcke, and Erhard Rahm. 2024 · 2024
Cited alongside, same era.
Grag: Graph retrieval-augmented generation
Yuntong Hu, Zhihan Lei, Zheng Zhang, Bo Pan, Chen Ling, and Liang Zhao. 2024 · 2024
Cited alongside, same era.
Scaling down to scale up: A cost-benefit analysis of replacing openai’s llm with open source slms in production
Chandra Irugalbandara, Ashish Mahendra, Roland Daynauth, Tharuka Kasthuri Arachchige, Jayanaka Dantanarayana, Krisztian Flautner, Lingjia Tang, Yiping Kang, and Jason Mars. 2024 · 2024
Cited alongside, same era.
Graph chain-of-thought: Augmenting large language models by reasoning on graphs
Graph retrieval-augmented generation: A survey
Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang. 2024 · 2024
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Is semantic chunking worth the computational cost?
Renyi Qu, Ruixuan Tu, and Forrest Bao. 2024 · 2024
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Knowledge graph based agent for complex, knowledge-intensive qa in medicine
Xiaorui Su, Yibo Wang, Shanghua Gao, Xiaolong Liu, Valentina Giunchiglia, Djork-Arné Clevert, and Marinka Zitnik. 2024 · 2024
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Temporal knowledge graph reasoning with dynamic memory enhancement
Fuwei Zhang, Zhao Zhang, Fuzhen Zhuang, Yu Zhao, Deqing Wang, and Hongwei Zheng. 2024 · 2024
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From local to global: A graph rag approach to query-focused summarization
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Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, and Jiawei Han. 2024 · 2024
Cited alongside, same era.
Taewoon Kim, Vincent François-Lavet, and Michael Cochez. 2024 · 2024
Cited alongside, same era.
RAGTruth: A hallucination corpus for developing trustworthy retrieval-augmented language models
Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, and Tong Zhang. 2024 · 2024
Cited alongside, same era.
Retrieval-augmented generation for natural language processing: A survey
Shangyu Wu, Ying Xiong, Yufei Cui, Haolun Wu, Can Chen, Ye Yuan, Lianming Huang, Xue Liu, Tei-Wei Kuo, Nan Guan, et al. 2024a
Cited in the paper.
Cotkr: Chain-of-thought enhanced knowledge rewriting for complex knowledge graph question answering
Yike Wu, Yi Huang, Nan Hu, Yuncheng Hua, Guilin Qi, Jiaoyan Chen, and Jeff Z. Pan. 2024b
Cited in the paper.
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. 2025 · 2025
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A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025 · 2025
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Redeep: Detecting hallucination in retrieval-augmented generation via mechanistic interpretability
Zhongxiang Sun, Xiaoxue Zang, Kai Zheng, Yang Song, Jun Xu, Xiao Zhang, Weijie Yu, Yang Song, and Han Li. 2025 · 2025
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