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Neural approaches to ranking based on pre-trained language models are highly effective in ad-hoc search.
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Multi-Stage Document Ranking with BERT
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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
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Distilling Dense Representations for Ranking using Tightly-Coupled Teachers
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Neural Ranking Models with Weak Supervision. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (Shinjuku, Tokyo, Japan) (SIGIR ’17) . Association for Computing Machinery, New York, NY, USA, 65–74
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Overview of the TREC 2019 Deep Learning Track. In 28th International Text Retrieval Conference, TREC 2019, Gaithersburg, Maryland, USA (NIST Special Publication) , Ellen M. Voorhees and Angela Ellis (Eds.). National Institute of Standards and Technology (NIST)
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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 · 2019
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Extracting Training Data from Large Language Models. In USENIX Security Symposium
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Overview of the TREC 2020 Deep Learning Track. In Proceedings of the 29th Text REtrieval Conference, TREC 2020, Virtual Event, Gaithersburg, MD, USA, November 16-20, 2020 (NIST Special Publication, Vol. 1266) , Ellen M. Voorhees and Angela Ellis (Eds.). National Institute of Standards and Technology (NIST)
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 2020 · 2020
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Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020 , Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 6769–6781
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. 2022 · 2022
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023 · 2023
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Detecting Pretraining Data from Large Language Models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke S. Zettlemoyer. 2023 · 2023
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Document Ranking with a Pretrained Sequence-to-Sequence Model. In Findings of the Association for Computational Linguistics: EMNLP 2020 , Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 708–718
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020 · 2020
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Rethink Training of BERT Rerankers in Multi-stage Retrieval Pipeline. In Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 – April 1, 2021, Proceedings, Part II . Springer-Verlag, Berlin, Heidelberg, 280–286
Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021 · 2021
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Searching for scientific evidence in a pandemic: An overview of TREC-COVID
Kirk Roberts, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, Kyle Lo, Ian Soboroff, Ellen Voorhees, Lucy Lu Wang, and William R. Hersh. 2021 · 2021
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Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew 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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From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22) . Association for Computing Machinery, New York, NY, USA, 2353–2359
Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. 2022 · 2022
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How Train–Test Leakage Affects Zero-Shot Retrieval. In String Processing and Information Retrieval: 29th International Symposium, SPIRE 2022, Concepción, Chile, November 8–10, 2022, Proceedings (Concepción, Chile). Springer-Verlag, Berlin, Heidelberg, 147–161
Maik Fröbe, Christopher Akiki, Martin Potthast, and Matthias Hagen. 2022 · 2022
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Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking. In Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part I (Stavanger, Norway). Springer-Verlag, Berlin, Heidelberg, 655–670
Ronak Pradeep, Yuqi Liu, Xinyu Zhang, Yilin Li, Andrew Yates, and Jimmy Lin. 2022 · 2022
Cited alongside, same era.
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 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 14918–14937
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, 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, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Zephyr: Direct Distillation of LM Alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2023 · 2023
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Rethinking benchmark and contamination for language models with rephrased samples
Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, Joseph E Gonzalez, and Ion Stoica. 2023 · 2023
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OpenAI, Josh Achiam, and et al. 2024 · 2024
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Ferdinand Schlatt, Maik Fröbe, Harrisen Scells, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, and Matthias Hagen. 2024 · 2024
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