Fetching the paper…
Reading the bibliography…
Query Reformulation (QR) is a set of techniques used to transform a user's original search query to a text that better aligns with the user's intent and improves their search experience.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Umass at trec 2004: Novelty and hard
Nasreen Abdul-Jaleel, James Allan, W Bruce Croft, Fernando Diaz, Leah Larkey, Xiaoyan Li, Mark D Smucker, and Courtney Wade. 2004 · 2004
Earlier work this paper cites.
Comparing rank and score combination methods for data fusion in information retrieval
D. Frank Hsu and Isak Taksa. 2005 · 2005
Earlier work this paper cites.
Combining multiple resources, evidences and criteria for genomic information retrieval
Luo Si, Jie Lu, and Jamie Callan. 2006 · 2006
Earlier work this paper cites.
A survey of automatic query expansion in information retrieval
Claudio Carpineto and Giovanni Romano. 2012 · 2012
Earlier work this paper cites.
Learning lexicon models from search logs for query expansion
Jianfeng Gao, Shasha Xie, Xiaodong He, and Alnur Ali. 2012 · 2012
Earlier work this paper cites.
Ms marco: A human-generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Earlier work this paper cites.
Dbpedia-entity v2: a test collection for entity search
Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, and Jamie Callan. 2017 · 2017
Earlier work this paper cites.
Identifying Well-formed Natural Language Questions
Manaal Faruqui and Dipanjan Das. 2018 · 2018
Earlier work this paper cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
Earlier work this paper cites.
Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019 · 2019
Earlier work this paper cites.
Fairness and abstraction in sociotechnical systems
Andrew D Selbst, Danah Boyd, Sorelle A Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019 · 2019
Earlier work this paper cites.
Touché: First shared task on argument retrieval
Alexander Bondarenko, Matthias Hagen, Martin Potthast, Henning Wachsmuth, Meriem Beloucif, Chris Biemann, Alexander Panchenko, and Benno Stein. 2020 · 2020
Earlier work this paper cites.
How to ask better questions? a large-scale multi-domain dataset for rewriting ill-formed questions
Zewei Chu, Mingda Chen, Jing Chen, Miaosen Wang, Kevin Gimpel, Manaal Faruqui, and Xiance Si. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2020 · 2020
Cited alongside, same era.
Simplified data wrangling with ir_datasets
Sean MacAvaney, Andrew Yates, Sergey Feldman, Doug Downey, Arman Cohan, and Nazli Goharian. 2021 · 2021
Cited alongside, same era.
Pyterrier: Declarative experimentation in python from bm25 to dense retrieval
Craig Macdonald, Nicola Tonellotto, Sean MacAvaney, and Iadh Ounis. 2021 · 2021
Cited alongside, same era.
Diversity driven query rewriting in search advertising
Akash Kumar Mohankumar, Nikit Begwani, and Amit Singh. 2021 · 2021
Cited alongside, same era.
The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models
Ronak Pradeep, Rodrigo Nogueira, and Jimmy J. Lin. 2021 · 2021
Cited alongside, same era.
Doc2query–: When less is more
Mitko Gospodinov, Sean MacAvaney, and Craig Macdonald. 2023 · 2023
Later among the works it cites.
Query expansion by prompting large language models
Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
Later among the works it cites.
ConvGQR: Generative query reformulation for conversational search
Fengran Mo, Kelong Mao, Yutao Zhu, Yihong Wu, Kaiyu Huang, and Jian-Yun Nie. 2023 · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Beir: A heterogeneous 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.
Improving query representations for dense retrieval with pseudo relevance feedback
HongChien Yu, Chenyan Xiong, and Jamie Callan. 2021 · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
Ask me anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, and Christopher Re. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
On decoding strategies for neural text generators
Gian Wiher, Clara Meister, and Ryan Cotterell. 2022 · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2023 · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Later among the works it cites.
Query2doc: Query expansion with large language models
Liang Wang, Nan Yang, and Furu Wei. 2023a · 2023
Later among the works it cites.
Generative query reformulation for effective adhoc search
Xiao Wang, Sean MacAvaney, Craig Macdonald, and Iadh Ounis. 2023c · 2023
Later among the works it cites.
Is your search query well-formed? a natural query understanding for patent prior art search
Renukswamy Chikkamath, Deepak Rastogi, Mahesh Maan, and Markus Endres. 2024 · 2024
Closest in time.
Genqrensemble: Zero-shot llm ensemble prompting for generative query reformulation
Kaustubh D. Dhole and Eugene Agichtein. 2024 · 2024
Closest in time.
Queryexplorer: An interactive query generation assistant for search and exploration
Kaustubh D. Dhole, Shivam Bajaj, Ramraj Chandradevan, and Eugene Agichtein. 2024 · 2024
Closest in time.
Corpus-steered query expansion with large language models
Yibin Lei, Yu Cao, Tianyi Zhou, Tao Shen, and Andrew Yates. 2024 · 2024
Closest in time.
Generative relevance feedback with large language models
Iain Mackie, Shubham Chatterjee, and Jeffrey Dalton. 2023 · 2031
Closest in time.