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Dense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space.
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Language Models are Few-Shot Learners
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Overview of the TREC 2020 Deep Learning Track. In Proceedings of the Twenty-Ninth Text REtrieval Conference, TREC 2020, Virtual Event [Gaithersburg, Maryland, USA], November 16-20, 2020 (NIST Special Publication, Vol. 1266) , Ellen M. Voorhees and Angela Ellis (Eds.). National Institute of Standards and Technology (NIST)
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Colbert: Efficient and effective passage search via contextualized late interaction over bert. In SIGIR . ACM, 39–48
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Document ranking with a pretrained sequence-to-sequence model
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Exploring the limits of transfer learning with a unified text-to-text transformer
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MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
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Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling. In SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 113–122
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The Power of Scale for Parameter-Efficient Prompt Tuning. In EMNLP . Association for Computational Linguistics, 3045–3059
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Prefix-Tuning: Optimizing Continuous Prompts for Generation. In ACL/IJCNLP . Association for Computational Linguistics, 4582–4597
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Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations. In SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 2356–2362
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True Few-Shot Learning with Language Models. In NeurIPS . 11054–11070
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RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021 , Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, 5835–5847
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021 · 2021
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RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 2825–2835
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InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers
Leonid Boytsov, Preksha Patel, Vivek Sourabh, Riddhi Nisar, Sayani Kundu, Ramya Ramanathan, and Eric Nyberg. 2023 · 2023
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Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
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Eˆ2VPT: An Effective and Efficient Approach for Visual Prompt Tuning
Cheng Han, Qifan Wang, Yiming Cui, Zhiwen Cao, Wenguan Wang, Siyuan Qi, and Dongfang Liu. 2023 · 2023
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Cited alongside, same era.
Colbertv2: Effective and efficient retrieval via lightweight late interaction
Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. 2021 · 2021
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How many data points is a prompt worth?
Teven Le Scao and Alexander M Rush. 2021 · 2021
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Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021 , Paola Merlo, Jörg Tiedemann, and Reut Tsarfaty (Eds.). Association for Computational Linguistics, 255–269
Timo Schick and Hinrich Schütze. 2021a · 2021
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It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021 , Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, 2339–2352
Timo Schick and Hinrich Schütze. 2021b · 2021
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BEIR: A heterogenous benchmark for zero-shot evaluation of information retrieval models
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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InPars: Unsupervised Dataset Generation for Information Retrieval. In SIGIR . ACM, 2387–2392
Luiz Henrique Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Frassetto Nogueira. 2022 · 2022
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Promptagator: Few-shot dense retrieval from 8 examples
Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B Hall, and Ming-Wei Chang. 2022 · 2022
Cited alongside, same era.
Precise Zero-Shot Dense Retrieval without Relevance Labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2022 · 2022
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Zhiqi Huang, Hansi Zeng, Hamed Zamani, and James Allan. 2023 · 2023
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InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Vitor Jeronymo, Luiz Henrique Bonifacio, Hugo Abonizio, Marzieh Fadaee, Roberto de Alencar Lotufo, Jakub Zavrel, and Rodrigo Frassetto Nogueira. 2023 · 2023
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Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training. In Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023 , Anna Rogers, Jordan L. Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, 10932–10940
Yibin Lei, Liang Ding, Yu Cao, Changtong Zan, Andrew Yates, and Dacheng Tao. 2023 · 2023
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How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval
Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, and Xilun Chen. 2023 · 2023
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Zero-Shot Listwise Document Reranking with a Large Language Model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin. 2023 · 2023
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Learning to Compress Prompts with Gist Tokens
Jesse Mu, Xiang Lisa Li, and Noah D. Goodman. 2023 · 2023
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RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023 · 2023
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
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Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 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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Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning. In ICLR . OpenReview.net
Zhen Wang, Rameswar Panda, Leonid Karlinsky, Rogério Feris, Huan Sun, and Yoon Kim. 2023 · 2023
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Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (Eds.)
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2023 · 2023
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Prompt Learns Prompt: Exploring Knowledge-Aware Generative Prompt Collaboration For Video Captioning. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China . ijcai.org, 1622–1630
Liqi Yan, Cheng Han, Zenglin Xu, Dongfang Liu, and Qifan Wang. 2023 · 2023
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MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction. In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics, ACL 2023 . Association for Computational Linguistics
Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, and Dongfang Liu. 2023 · 2023
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Resources for Brewing BEIR: Reproducible Reference Models and Statistical Analyses
Ehsan Kamalloo, Nandan Thakur, Carlos Lassance, Xueguang Ma, Jheng-Hong Yang, and Jimmy Lin. 2024 · 2024
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