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Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often struggle to meet the complex and diverse needs of real-world industrial applications.
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WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
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Murag: Multimodal retrieval-augmented generator for open question answering over images and text, 2022
Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, and William W. Cohen · 2022
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Precise zero-shot dense retrieval without relevance labels, 2022
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan · 2022
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Large language models are zero-shot reasoners
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End-to-end table question answering via retrieval-augmented generation, 2022
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Musique: Multihop questions via single-hop question composition
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Open australian legal qa, 2023
Umar Butler · 2023
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Advanced rag 01: Small-to-big retrieval
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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Songyang Zhang, Kai Chen, Zongwen Shen, and Jidong Ge · 2023
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Zhangyin Feng, Xiaocheng Feng, Dezhi Zhao, Maojin Yang, and Bing Qin · 2023
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Y Gao, Y Xiong, X Gao, K Jia, J Pan, Y Bi, Y Dai, J Sun, and H Wang · 2023
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H. Jiang, Q. Wu, X. Luo, D. Li, C.-Y. Lin, Y. Yang, and L. Qiu · 2023
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Active retrieval augmented generation, 2023
Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp, 2023
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Leveraging structured information for explainable multi-hop question answering and reasoning, 2023
Ruosen Li and Xinya Du · 2023
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Long-context llms struggle with long in-context learning, 2024
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Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Dhagash Mehta, Stefano Pasquali, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, and Liang Zhao · 2024
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Holmes: Hyper-relational knowledge graphs for multi-hop question answering using llms, 2024
Pranoy Panda, Ankush Agarwal, Chaitanya Devaguptapu, Manohar Kaul, and Prathosh A P · 2024
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Eratta: Extreme rag for table to answers with large language models, 2024
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Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, and Jian Guo · 2024
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Phi-4 technical report, 2024
Phi-4 Team · 2024
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Qwen Team · 2024
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Hallucination is inevitable: An innate limitation of large language models, 2024
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli · 2024
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Crag – comprehensive rag benchmark, 2024
Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Daniel Gui, Ziran Will Jiang, Ziyu Jiang, Lingkun Kong, Brian Moran, Jiaqi Wang, Yifan Ethan Xu, An Yan, Chenyu Yang, Eting Yuan, Hanwen Zha, Nan Tang, Lei Chen, Nicolas Scheffer, Yue Liu, Nirav Shah, Rakesh Wanga, Anuj Kumar, Wen tau Yih, and Xin Luna Dong · 2024
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End-to-end beam retrieval for multi-hop question answering, 2024
Jiahao Zhang, Haiyang Zhang, Dongmei Zhang, Yong Liu, and Shen Huang · 2024
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Retrieval-augmented generation for ai-generated content: A survey
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Take a step back: Evoking reasoning via abstraction in large language models, 2024
Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H. Chi, Quoc V Le, and Denny Zhou · 2024
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Mix-of-granularity: Optimize the chunking granularity for retrieval-augmented generation, 2024
Zijie Zhong, Hanwen Liu, Xiaoya Cui, Xiaofan Zhang, and Zengchang Qin · 2024
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