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Retrieval-augmented generation (RAG) techniques have emerged as a promising solution to enhance the reliability of large language models (LLMs) by addressing issues like hallucinations, outdated knowledge, and domain adaptation.
Language models are few-shot learners
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Cited alongside, same era.
Replug: Retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2023 · 2023
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Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval
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Caseformer: Pre-training for Legal Case Retrieval
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Llama: Open and efficient foundation language models
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Self-knowledge guided retrieval augmentation for large language models
Yile Wang, Peng Li, Maosong Sun, and Yang Liu. 2023 · 2023
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Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
Ingeol Baek, Hwan Chang, Byeongjeong Kim, Jimin Lee, and Hwanhee Lee. 2024 · 2024
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From local to global: A graph rag approach to query-focused summarization
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Weihang Su, Yiran Hu, Anzhe Xie, Qingyao Ai, Quezi Bing, Ning Zheng, Yun Liu, Weixing Shen, and Yiqun Liu. 2024a · 2024
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Mitigating entity-level hallucination in large language models. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region . 23–31
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DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand, 12991–13013
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Unsupervised real-time hallucination detection based on the internal states of large language models
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Knowledge Editing through Chain-of-Thought
Changyue Wang, Weihang Su, Qingyao Ai, and Yiqun Liu. 2024b · 2024
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LeKUBE: A Legal Knowledge Update BEnchmark
Changyue Wang, Weihang Su, Hu Yiran, Qingyao Ai, Yueyue Wu, Cheng Luo, Yiqun Liu, Min Zhang, and Shaoping Ma. 2024c · 2024
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Rat: Retrieval augmented thoughts elicit context-aware reasoning in long-horizon generation
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Continual learning for large language models: A survey
Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, and Gholamreza Haffari. 2024 · 2024
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Seakr: Self-aware knowledge retrieval for adaptive retrieval augmented generation
Zijun Yao, Weijian Qi, Liangming Pan, Shulin Cao, Linmei Hu, Weichuan Liu, Lei Hou, and Juanzi Li. 2024 · 2024
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Rankrag: Unifying context ranking with retrieval-augmented generation in llms
Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, and Bryan Catanzaro. 2024 · 2024
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Neuron-Level Knowledge Attribution in Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 3267–3280
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Decoupling Knowledge and Context: An Efficient and Effective Retrieval Augmented Generation Framework via Cross Attention. In Proceedings of the ACM on Web Conference 2025
Qian Dong, Qingyao Ai, Hongning Wang, Yiding Liu, Haitao Li, Weihang Su, Yiqun Liu, Tat-Seng Chua, and Shaoping Ma. 2025 · 2025
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96.55% of Content Gets No Traffic From Google. Here’s How to Be in the Other 3.45% [New Research for 2023]
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