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Large Language Models (LLMs) have significantly impacted many facets of natural language processing and information retrieval.
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Algorithm 65: find,
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Okapi at TREC-3,
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Efficient query evaluation using a two-level retrieval process,
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Overview of the TREC 2019 deep learning track,
N. Craswell, B. Mitra, E. Yilmaz, D. Campos, E. M. Voorhees, · 2003
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Terrier information retrieval platform,
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Efficient document retrieval in main memory,
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MS MARCO: A human generated machine reading comprehension dataset,
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Attention is all you need,
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BERT: pre-training of deep bidirectional transformers for language understanding,
J. Devlin, M. Chang, K. Lee, K. Toutanova, · 2019
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CEDR: contextualized embeddings for document ranking,
S. MacAvaney, A. Yates, A. Cohan, N. Goharian, · 2019
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Dense passage retrieval for open-domain question answering,
V. Karpukhin, B. Oguz, S. Min, P. S. H. Lewis, L. Wu, S. Edunov, D. Chen, W. Yih, · 2020
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Language models are few-shot learners,
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Retrieval-augmented generation for knowledge-intensive NLP tasks,
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Exploring the limits of transfer learning with a unified text-to-text transformer,
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Expansion via prediction of importance with contextualization,
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Document ranking with a pretrained sequence-to-sequence model,
R. F. Nogueira, Z. Jiang, R. Pradeep, J. Lin, · 2020
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Overview of the TREC 2020 deep learning track,
N. Craswell, B. Mitra, E. Yilmaz, D. Campos, · 2020
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Trec-covid,
E. M. Voorhees, T. Alam, S. Bedrick, D. Demner-Fushman, W. R. Hersh, K. Lo, K. Roberts, I. Soboroff, L. L. Wang, · 2020
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Overview of touché 2020: Argument retrieval,
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Rankzephyr: Effective and robust zero-shot listwise reranking is a breeze!,
R. Pradeep, S. Sharifymoghaddam, J. Lin, · 2023
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Rank-without-gpt: Building gpt-independent listwise rerankers on open-source large language models,
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R. Tang, X. Zhang, X. Ma, J. Lin, F. Ture, · 2023
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Rankt5: Fine-tuning t5 for text ranking with ranking losses,
H. Zhuang, Z. Qin, R. Jagerman, K. Hui, J. Ma, J. Lu, J. Ni, X. Wang, M. Bendersky, · 2023
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Zero-shot listwise document reranking with a large language model,
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The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models,
R. Pradeep, R. F. Nogueira, J. Lin, · 2021
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SPLADE: sparse lexical and expansion model for first stage ranking,
T. Formal, B. Piwowarski, S. Clinchant, · 2021
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BEIR: A heterogenous benchmark for zero-shot evaluation of information retrieval models,
N. Thakur, N. Reimers, A. Rücklé, A. Srivastava, I. Gurevych, · 2021
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Scaling instruction-finetuned language models,
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Few-shot learning with retrieval augmented language models,
G. Izacard, P. S. H. Lewis, M. Lomeli, L. Hosseini, F. Petroni, T. Schick, J. Dwivedi-Yu, A. Joulin, S. Riedel, E. Grave, · 2022
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Squeezing water from a stone: A bag of tricks for further improving cross-encoder effectiveness for reranking,
R. Pradeep, Y. Liu, X. Zhang, Y. Li, A. Yates, J. Lin, · 2022
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X. Ma, X. Zhang, R. Pradeep, J. Lin, · 2023
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Rankvicuna: Zero-shot listwise document reranking with open-source large language models,
R. Pradeep, S. Sharifymoghaddam, J. Lin, · 2023
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M. S. Tamber, R. Pradeep, J. Lin, · 2023
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Large language models are effective text rankers with pairwise ranking prompting,
Z. Qin, R. Jagerman, K. Hui, H. Zhuang, J. Wu, J. Shen, T. Liu, J. Liu, D. Metzler, X. Wang, M. Bendersky, · 2023
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A setwise approach for effective and highly efficient zero-shot ranking with large language models,
S. Zhuang, H. Zhuang, B. Koopman, G. Zuccon, · 2023
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Efficient neural ranking using forward indexes and lightweight encoders,
J. Leonhardt, H. Müller, K. Rudra, M. Khosla, A. Anand, A. Anand, · 2023
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Optimizing guided traversal for fast learned sparse retrieval,
Y. Qiao, Y. Yang, H. Lin, T. Yang, · 2023
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2023
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2024
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2024
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Ranked list truncation for large language model-based re-ranking,
C. Meng, N. Arabzadeh, A. Askari, M. Aliannejadi, M. de Rijke, · 2024
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