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This work considers a black-box threat model in which adversaries attempt to propagate arbitrary non-relevant content in search.
Large language models can accurately predict searcher preferences. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1930–1940
Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 2024 · 1940
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WWW’18 Open Challenge: Financial Opinion Mining and Question Answering. In Companion Proceedings of the The Web Conference 2018 (Lyon, France) (WWW ’18) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1941–1942
Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018 · 1942
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Introduction to Information Retrieval
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008 · 2008
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
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Explaining and Harnessing Adversarial Examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2016 · 2016
Earlier work this paper cites.
A Full-Text Learning to Rank Dataset for Medical Information Retrieval. In Advances in Information Retrieval , Nicola Ferro, Fabio Crestani, Marie-Francine Moens, Josiane Mothe, Fabrizio Silvestri, Giorgio Maria Di Nunzio, Claudia Hauff, and Gianmaria Silvello (Eds.). Springer International Publishing, Cham, 716–722
Vera Boteva, Demian Gholipour, Artem Sokolov, and Stefan Riezler. 2016 · 2016
Earlier work this paper cites.
Delving into Transferable Adversarial Examples and Black-box Attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. 2016 · 2016
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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. 2016 · 2016
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Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2017 · 2017
Earlier work this paper cites.
DBpedia-Entity v2: A Test Collection for Entity Search. 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, 1265–1268
Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, and Jamie Callan. 2017 · 2017
Earlier work this paper cites.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
Earlier work this paper cites.
Practical Black-Box Attacks against Machine Learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security . 506–519
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017 · 2017
Earlier work this paper cites.
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Overview of the TREC 2019 Deep Learning Track. In Proceedings of the Twenty-Eighth Text REtrieval Conference Proceedings (TREC 2019) . Gaithersburg, Maryland
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Rodrigo Nogueira and Kyunghyun Cho. 2019 · 2019
Earlier work this paper cites.
Multi-Stage Document Ranking with BERT
Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019 · 2019
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, Hong Kong, China, 3982–3992
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Overview of the TREC 2020 Deep Learning Track. In Proceedings of the Twenty-Ninth Text REtrieval Conference Proceedings (TREC 2020) . Gaithersburg, Maryland
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 2020 · 2020
Earlier work this paper cites.
CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian, Massimiliano Ciaramita, and Markus Leippold. 2020 · 2020
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spaCy: Industrial-strength Natural Language Processing in Python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020 · 2020
Cited alongside, same era.
Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 6769–6781
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Cited alongside, same era.
Document Ranking with a Pretrained Sequence-to-Sequence Model
Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. 2020 · 2020
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023b · 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 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). 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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Pre-processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering. In Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part III (Dublin, Ireland). Springer-Verlag, Berlin, Heidelberg, 163–176
Manveer Singh Tamber, Ronak Pradeep, and Jimmy Lin. 2023a · 2023
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Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models
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Nisarg Raval and Manisha Verma. 2020 · 2020
Cited alongside, same era.
Adversarial Semantic Collisions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 4198–4210
Congzheng Song, Alexander Rush, and Vitaly Shmatikov. 2020 · 2020
Cited alongside, same era.
Fact or Fiction: Verifying Scientific Claims. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 7534–7550
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020 · 2020
Cited alongside, same era.
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020 · 2020
Cited alongside, same era.
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 2288–2292
Thibault Formal, Benjamin Piwowarski, and Stéphane Clinchant. 2021 · 2021
Cited alongside, same era.
Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup. In Proceedings of the 6th Workshop on Representation Learning for NLP
Luyu Gao, Yunyi Zhang, Jiawei Han, and Jamie Callan. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
TREC-COVID: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum , Vol. 54. ACM New York, NY, USA, 1–12
Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021 · 2021
Cited alongside, same era.
Manveer Singh Tamber, Ronak Pradeep, and Jimmy Lin. 2023b · 2023
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PRADA: Practical Black-box Adversarial Attacks against Neural Ranking Models
Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten De Rijke, Yixing Fan, and Xueqi Cheng. 2023 · 2023
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Poisoning Retrieval Corpora by Injecting Adversarial Passages
Zexuan Zhong, Ziqing Huang, Alexander Wettig, and Danqi Chen. 2023 · 2023
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RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2308–2313
Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
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LLMs can be Fooled into Labelling a Document as Relevant: best café near me; this paper is perfectly relevant. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (Tokyo, Japan) (SIGIR-AP 2024) . Association for Computing Machinery, New York, NY, USA, 32–41
Marwah Alaofi, Paul Thomas, Falk Scholer, and Mark Sanderson. 2024 · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 8832–8840
Yu-An Liu, Ruqing Zhang, Mingkun Zhang, Wei Chen, Maarten de Rijke, Jiafeng Guo, and Xueqi Cheng. 2024 · 2024
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Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models
Luke Merrick, Danmei Xu, Gaurav Nuti, and Daniel Campos. 2024 · 2024
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Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models. In European Conference on Information Retrieval . Springer, 286–302
Andrew Parry, Maik Fröbe, Sean MacAvaney, Martin Potthast, and Matthias Hagen. 2024 · 2024
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Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents
Avital Shafran, Roei Schuster, and Vitaly Shmatikov. 2024 · 2024
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Can’t Hide Behind the API: Stealing Black-Box Commercial Embedding Models
Manveer Singh Tamber, Jasper Xian, and Jimmy Lin. 2024 · 2024
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Hexiang Tan, Fei Sun, Wanli Yang, Yuanzhuo Wang, Qi Cao, and Xueqi Cheng. 2024 · 2024
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UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessor
Shivani Upadhyay, Ronak Pradeep, Nandan Thakur, Nick Craswell, and Jimmy Lin. 2024 · 2024
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Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, et al · 2024
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C-pack: Packed resources for general chinese embeddings. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 641–649
Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. 2024 · 2024
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PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
Wei Zou, Runpeng Geng, Binghui Wang, and Jinyuan Jia. 2024 · 2024
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Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . 2025–2039
Jiawei Liu, Yangyang Kang, Di Tang, Kaisong Song, Changlong Sun, Xiaofeng Wang, Wei Lu, and Xiaozhong Liu. 2022 · 2039
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