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Recently, there has been increasing interest in applying large language models (LLMs) as zero-shot passage rankers.
Rodrigo Frassetto Nogueira and Kyunghyun Cho. 2019 · 1901
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Learning to rank for information retrieval
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The Probabilistic Relevance Framework: BM25 and Beyond
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. In Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016 co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, December 9, 2016 (CEUR Workshop Proceedings, Vol. 1773) , Tarek Richard Besold, Antoine Bordes, Artur S. d’Avila Garcez, and Greg Wayne (Eds.). CEUR-WS.org
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FEVER: a Large-scale Dataset for Fact Extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers) , Marilyn A. Walker, Heng Ji, and Amanda Stent (Eds.). Association for Computational Linguistics, 809–819
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In EMNLP . Association for Computational Linguistics, 2369–2380
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Natural Questions: a Benchmark for Question Answering Research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Overview of the TREC 2020 Deep Learning Track. In TREC (NIST Special Publication, Vol. 1266) . National Institute of Standards and Technology (NIST)
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 2020a · 2020
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Overview of the TREC 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2020b · 2020
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Document Ranking with a Pretrained Sequence-to-Sequence Model. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020 (Findings of ACL, Vol. EMNLP 2020) , Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 708–718
Rodrigo Frassetto Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020 · 2020
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Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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Holistic Evaluation of Language Models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. 2022 · 2022
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What Makes Good In-Context Examples for GPT-3?. In DeeLIO@ACL . Association for Computational Linguistics, 100–114
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
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DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing. In ICLR . OpenReview.net
Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2023 · 2023
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Unified Demonstration Retriever for In-Context Learning. In ACL (1) . Association for Computational Linguistics, 4644–4668
Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, and Xipeng Qiu. 2023 · 2023
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Dr.ICL: Demonstration-Retrieved In-context Learning
Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Seyed Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, and Vincent Y. Zhao. 2023 · 2023
Later among the works it cites.
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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Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity. In ACL (1) . Association for Computational Linguistics, 8086–8098
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
Cited alongside, same era.
Learning To Retrieve Prompts for In-Context Learning. In NAACL-HLT . Association for Computational Linguistics, 2655–2671
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
Cited alongside, same era.
Improving Passage Retrieval with Zero-Shot Question Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, 3781–3797
Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Text Embeddings by Weakly-Supervised Contrastive Pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
Cited alongside, same era.
Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
Cited alongside, same era.
Active Example Selection for In-Context Learning. In EMNLP . Association for Computational Linguistics, 9134–9148
Yiming Zhang, Shi Feng, and Chenhao Tan. 2022 · 2022
Cited alongside, same era.
In-context Examples Selection for Machine Translation. 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, 8857–8873
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023 · 2023
Cited alongside, same era.
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation. In EMNLP . Association for Computational Linguistics, 12318–12337
Daixuan Cheng, Shaohan Huang, Junyu Bi, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Furu Wei, Weiwei Deng, and Qi Zhang. 2023 · 2023
Cited alongside, same era.
PaRaDe: Passage Ranking using Demonstrations with LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 14242–14252
Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, and Kai Hui. 2023 · 2023
Cited alongside, same era.
Alexander Scarlatos and Andrew S. Lan. 2023 · 2023
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Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, 14918–14937
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023 · 2023
Later among the works it cites.
Learning to Retrieve In-Context Examples for Large Language Models
Liang Wang, Nan Yang, and Furu Wei. 2023 · 2023
Later among the works it cites.
Large Language Models for Information Retrieval: A Survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu, Le Yan, Xuanhui Wang, and Michael Bendersky. 2023b · 2023
Later among the works it cites.
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking. In Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, 8807–8817
Shengyao Zhuang, Bing Liu, Bevan Koopman, and Guido Zuccon. 2023a · 2023
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Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
Zhe Xu, Daoyuan Chen, Jiayi Kuang, Zihao Yi, Yaliang Li, and Ying Shen. 2024 · 2024
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INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zhicheng Dou, Zheng Liu, and Ji-Rong Wen. 2024 · 2024
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