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Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
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Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M Voorhees. 2020 · 2003
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Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. 2020 · 2003
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Honglei Zhuang, Zhen Qin, Shuguang Han, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2021 · 2021
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Task-aware retrieval with instructions
Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, and Wen-tau Yih. 2022 · 2022
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Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. 2022 · 2022
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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, et al. 2022 · 2022
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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
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Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2022 · 2022
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Holistic evaluation of language models
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Zero-shot listwise document reranking with a large language model
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Gpt-4 technical report. arxiv 2303.08774
R OpenAI. 2023 · 2023
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Soft prompt tuning for augmenting dense retrieval with large language models
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Large language models are effective text rankers with pairwise ranking prompting
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Cache & distil: Optimising api calls to large language models
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