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Retrieval Augmented Generation (RAG) has greatly improved the performance of Large Language Model (LLM) responses by grounding generation with context from existing documents.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 1910
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The TREC-8 question answering track
Ellen M. Voorhees and Dawn M. Tice · 2000
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Abstract Meaning Representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider · 2013
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Improving pairwise ranking for multi-label image classification
Yuncheng Li, Yale Song, and Jiebo Luo · 2017
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Leveraging knowledge graph for open-domain question answering
Jose Ortiz Costa and Anagha Kulkarni · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Exploiting edge features for graph neural networks
Liyu Gong and Qiang Cheng · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Learning to retrieve reasoning paths over wikipedia graph for question answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong · 2020
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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
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Document ranking with a pretrained sequence-to-sequence model
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin · 2020
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KG-FiD: Infusing knowledge graph in fusion-in-decoder for open-domain question answering
Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, and Michael Zeng · 2022
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Dsqa-llm: Domain-specific intelligent question answering based on large language model
Dengrong Huang, Zizhong Wei, Aizhen Yue, Xuan Zhao, Zhaoliang Chen, Rui Li, Kai Jiang, Bingxin Chang, Qilai Zhang, Sijia Zhang, et al · 2023
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Towards general text embeddings with multi-stage contrastive learning, 2023
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang · 2023
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Large language model is not a good few-shot information extractor, but a good reranker for hard samples!
Yubo Ma, Yixin Cao, Yong Hong, and Aixin Sun · 2023
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Rink: reader-inherited evidence reranker for table-and-text open domain question answering
Eunhwan Park, Sung-Min Lee, Dearyong Seo, Seonhoon Kim, Inho Kang, and Seung-Hoon Na · 2023
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Xuefeng Bai, Yulong Chen, Linfeng Song, and Yue Zhang · 2021
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Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Édouard Grave · 2021
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Docamr: Multi-sentence amr representation and evaluation
Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Timothy J. O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Fernández Astudillo, Radu Florian, Salim Roukos, and Nathan Schneider · 2021
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Graph pre-training for AMR parsing and generation
Xuefeng Bai, Yulong Chen, and Yue Zhang · 2022
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Re2G: Retrieve, rerank, generate
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, and Alfio Gliozzo · 2022
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Grape: Knowledge graph enhanced passage reader for open-domain question answering
Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, and Yanfang Ye · 2022
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RetroMAE: Pre-training retrieval-oriented transformers via masked auto-encoder
Zheng Liu and Yingxia Shao · 2022
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Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara · 2023
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Is chatgpt good at search? investigating large language models as re-ranking agents
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren · 2023
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Can chatgpt replace traditional kbqa models? an in-depth analysis of the question answering performance of the gpt llm family
Yiming Tan, Dehai Min, Yu Li, Wenbo Li, Nan Hu, Yongrui Chen, and Guilin Qi · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Exploiting Abstract Meaning Representation for open-domain question answering
Cunxiang Wang, Zhikun Xu, Qipeng Guo, Xiangkun Hu, Xuefeng Bai, Zheng Zhang, and Yue Zhang · 2023
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C-pack: Packaged resources to advance general chinese embedding, 2023
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff · 2023
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Rankt5: Fine-tuning t5 for text ranking with ranking losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, and Michael Bendersky · 2023
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Large language models are zero-shot rankers for recommender systems
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao · 2024
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