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Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 1906
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Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2019 · 1910
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The use of multiple measurements in taxonomic problems
Rory A. Fisher. 1936 · 1936
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Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R. Bowman, and Omer Levy. 2023 · 1952
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International application of a new probability algorithm for the diagnosis of coronary artery disease
Robert C. Detrano, András Jánosi, Walter Steinbrunn, Matthias Emil Pfisterer, Johann-Jakob Schmid, Sarbjit Sandhu, Kern Guppy, Stella Lee, and Victor Froelicher. 1989 · 1989
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Learning to parse database queries using inductive logic programming
John M. Zelle and Raymond J. Mooney. 1996 · 1996
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Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
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Mutual: A dataset for multi-turn dialogue reasoning
Leyang Cui, Yu Wu, Shujie Liu, Yue Zhang, and Ming Zhou. 2020 · 2004
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Xcopa: A multilingual dataset for causal commonsense reasoning
Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, Qianchu Liu, Ivan Vulić, and Anna Korhonen. 2020 · 2005
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Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2005 · 2005
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Ethos: an online hate speech detection dataset
Ioannis Mollas, Zoe Chrysopoulou, Stamatis Karlos, and Grigorios Tsoumakas. 2020 · 2006
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Xiaodong Song, and Jacob Steinhardt. 2020 · 2009
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Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2020 · 2009
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010 · 2010
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Alfworld: Aligning text and embodied environments for interactive learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. 2020 · 2010
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The winograd schema challenge
Hector J. Levesque, Ernest Davis, and L. Morgenstern. 2011 · 2011
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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
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Paraphrasing for style
Wei Xu, Alan Ritter, William B. Dolan, Ralph Grishman, and Colin Cherry. 2012 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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Ncbi disease corpus: a resource for disease name recognition and concept normalization
Rezarta Islamaj Doğan, Robert Leaman, and Zhiyong Lu. 2014 · 2014
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Learning to solve arithmetic word problems with verb categorization
Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman. 2014 · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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Parsing algebraic word problems into equations
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. 2015 · 2015
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The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems
Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau. 2015 · 2015
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cícero Nogueira dos Santos, Çaglar Gülçehre, and Bing Xiang. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Solving general arithmetic word problems
Subhro Roy and Dan Roth. 2016 · 2016
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Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Matthew Cer, Mona T. Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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Overview of the iwslt 2017 evaluation campaign
Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Niehues Jan, Stüker Sebastian, Sudoh Katsuitho, Yoshino Koichiro, and Federmann Christian. 2017 · 2017
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Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts
Franck Dernoncourt and Ji Young Lee. 2017 · 2017
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Creating training corpora for nlg micro-planners
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017 · 2017
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Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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The e2e dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondrej Dusek, and Verena Rieser. 2017 · 2017
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Biosses: a semantic sentence similarity estimation system for the biomedical domain
Gizem Sogancioglu, Hakime Öztürk, and Arzucan Özgür. 2017 · 2017
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“liar, liar pants on fire”: A new benchmark dataset for fake news detection
William Yang Wang. 2017 · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2017 · 2017
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Multiwoz-a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gasic. 2018 · 2018
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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Measuring the evolution of a scientific field through citation frames
David Jurgens, Srijan Kumar, Raine Hoover, Daniel A. McFarland, and Dan Jurafsky. 2018 · 2018
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Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Hajishirzi. 2018 · 2018
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018 · 2018
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Earlier work this paper cites.
When and why are pre-trained word embeddings useful for neural machine translation?
Ye Qi, Devendra Singh Sachan, Matthieu Felix, Sarguna Padmanabhan, and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Learning to few-shot learn across diverse natural language classification tasks
Trapit Bansal, Rishikesh Jha, and Andrew McCallum. 2019 · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine de Marneffe, Mandy Simons, and Judith Tonhauser. 2019 · 2019
Cited alongside, same era.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Cited alongside, same era.
Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Cited alongside, same era.
Dstc7 task 1: Noetic end-to-end response selection
Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, and Walter S. Lasecki. 2019 · 2019
Cited alongside, same era.
Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. 2023 · 2023
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Scone: Benchmarking negation reasoning in language models with fine-tuning and in-context learning
Jingyuan Selena She, Christopher Potts, Sam Bowman, and Atticus Geiger. 2023 · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
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Joint prompt optimization of stacked llms using variational inference
Alessandro Sordoni, Eric Yuan, Marc-Alexandre Côté, Matheus Pereira, Adam Trischler, Ziang Xiao, Arian Hosseini, Friederike Niedtner, and Nicolas Le Roux. 2023 · 2023
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Autohint: Automatic prompt optimization with hint generation
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Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 2019
Cited alongside, same era.
Semeval-2019 task 4: Hyperpartisan news detection
Johannes Kiesel, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, D. Corney, Benno Stein, and Martin Potthast. 2019 · 2019
Cited alongside, same era.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
Cited alongside, same era.
Boxnet: A deep learning method for 2d bounding box estimation from bird’s-eye view point cloud
Ehsan Nezhadarya, Yang Liu, and Bingbing Liu. 2019 · 2019
Cited alongside, same era.
Wic: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
Cited alongside, same era.
Social iqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, and Denis Charles. 2023 · 2023
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, et al. 2023 · 2023
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Flipkart product reviews with sentiment dataset
Nirali Vaghani and Mansi Thummar. 2023 · 2023
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2023
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Survival of the most influential prompts: Efficient black-box prompt search via clustering and pruning
Han Zhou, Xingchen Wan, Ivan Vulić, and Anna Korhonen. 2023 · 2023
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Promptwizard: Task-aware prompt optimization framework
Eshaan Agarwal, Joykirat Singh, Vivek Dani, Raghav Magazine, Tanuja Ganu, and Akshay Nambi. 2024 · 2024
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Direct preference optimization with an offset
Afra Amini, Tim Vieira, and Ryan Cotterell. 2024 · 2024
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What’s the magic word? a control theory of llm prompting
Aman Bhargava, Cameron Witkowski, Shi-Zhuo Looi, and Matt Thomson. 2024 · 2024
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PRompt optimization in multi-step tasks (PROMST): Integrating human feedback and heuristic-based sampling
Yongchao Chen, Jacob Arkin, Yilun Hao, Yang Zhang, Nicholas Roy, and Chuchu Fan. 2024 · 2024
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Black-box prompt optimization: Aligning large language models without model training
Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, and Minlie Huang. 2024 · 2024
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Hard prompts made interpretable: Sparse entropy regularization for prompt tuning with rl
Yunseon Choi, Sangmin Bae, Seonghyun Ban, Minchan Jeong, Chuheng Zhang, Lei Song, Li Zhao, Jiang Bian, and Kee-Eung Kim. 2024 · 2024
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, and Zhifang Sui. 2024a · 2024
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PACE: Improving prompt with actor-critic editing for large language model
Yihong Dong, Kangcheng Luo, Xue Jiang, Zhi Jin, and Ge Li. 2024b · 2024
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Ipo: Interpretable prompt optimization for vision-language models
Yingjun Du, Wenfang Sun, and Cees GM Snoek. 2024 · 2024
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Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. 2024 · 2024
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Automatic engineering of long prompts
Cho-Jui Hsieh, Si Si, Felix Yu, and Inderjit Dhillon. 2024 · 2024
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Yasaman Jafari, Dheeraj Mekala, Rose Yu, and Taylor Berg-Kirkpatrick. 2024 · 2024
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Prompt-a-video: Prompt your video diffusion model via preference-aligned llm
Yatai Ji, Jiacheng Zhang, Jie Wu, Shilong Zhang, Shoufa Chen, Chongjian GE, Peize Sun, Weifeng Chen, Wenqi Shao, Xuefeng Xiao, et al. 2024 · 2024
Later among the works it cites.
Apeer: Automatic prompt engineering enhances large language model reranking
Can Jin, Hongwu Peng, Shiyu Zhao, Zhenting Wang, Wujiang Xu, Ligong Han, Jiahui Zhao, Kai Zhong, Sanguthevar Rajasekaran, and Dimitris N. Metaxas. 2024 · 2024
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Doing personal laps: Llm-augmented dialogue construction for personalized multi-session conversational search
Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P De Vries, Jeff Dalton, and Faegheh Hasibi. 2024 · 2024
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Task facet learning: A structured approach to prompt optimization
Gurusha Juneja, Nagarajan Natarajan, Hua Li, Jian Jiao, and Amit Sharma. 2024 · 2024
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Dspy: Compiling declarative language model calls into self-improving pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, and Christopher Potts. 2024 · 2024
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Prewrite: Prompt rewriting with reinforcement learning
Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024 · 2024
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Sculpt: Systematic tuning of long prompts
Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal, Bishal Santra, Parag Agrawal, and Manish Gupta. 2024 · 2024
Later among the works it cites.
Prompt optimization with human feedback
Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, and Bryan Kian Hsiang Low. 2024 · 2024
Later among the works it cites.
Large language models as evolutionary optimizers
Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, and Yew Soon Ong. 2023 · 2024
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Prompt optimization via adversarial in-context learning
Xuan Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Xu Zhao, Nancy F. Chen, Kenji Kawaguchi, Michael Shieh, and Junxian He. 2024 · 2024
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Strings from the library of babel: Random sampling as a strong baseline for prompt optimisation
Yao Lu, Jiayi Wang, Raphael Tang, Sebastian Riedel, and Pontus Stenetorp. 2024 · 2024
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Improving text-to-image consistency via automatic prompt optimization
Oscar Mañas, Pietro Astolfi, Melissa Hall, Candace Ross, Jack Urbanek, Adina Williams, Aishwarya Agrawal, Adriana Romero-Soriano, and Michal Drozdzal. 2024 · 2024
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Rimon Melamed, Lucas H. McCabe, Tanay Wakhare, Yejin Kim, H. Howie Huang, and Enric Boix-Adsera. 2024 · 2024
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Glov: Guided large language models as implicit optimizers for vision language models
M Jehanzeb Mirza, Mengjie Zhao, Zhuoyuan Mao, Sivan Doveh, Wei Lin, Paul Gavrikov, Michael Dorkenwald, Shiqi Yang, Saurav Jha, Hiromi Wakaki, et al. 2024 · 2024
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Optimizing instructions and demonstrations for multi-stage language model programs
Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, and Omar Khattab. 2024 · 2024
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Plum: Prompt learning using metaheuristics
Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, KaShun Shum, Jipeng Zhang, Renjie Pi, and Tong Zhang. 2024 · 2024
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Tobias Schnabel and Jennifer Neville. 2024 · 2024
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Robustness-aware automatic prompt optimization
Zeru Shi, Zhenting Wang, Yongye Su, Weidi Luo, Fan Yang, and Yongfeng Zhang. 2024 · 2024
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024
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Survival of the safest: Towards secure prompt optimization through interleaved multi-objective evolution
Ankita Sinha, Wendi Cui, Kamalika Das, and Jiaxin Zhang. 2024 · 2024
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Teach better or show smarter? on instructions and exemplars in automatic prompt optimization
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, and Sercan O. Arik. 2024 · 2024
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Promptagent: Strategic planning with language models enables expert-level prompt optimization
Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, and Zhiting Hu. 2024a · 2024
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StraGo: Harnessing strategic guidance for prompt optimization
Yurong Wu, Yan Gao, Bin Benjamin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, and Linjun Yang. 2024 · 2024
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Jasper Xian, Saron Samuel, Faraz Khoubsirat, Ronak Pradeep, Md Arafat Sultan, Radu Florian, Salim Roukos, Avirup Sil, Christopher Potts, and Omar Khattab. 2024 · 2024
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Reprompting: automated chain-of-thought prompt inference through gibbs sampling
Weijia Xu, Andrzej Banburski-Fahey, and Nebojsa Jojic. 2024 · 2024
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Dual-phase accelerated prompt optimization
Muchen Yang, Moxin Li, Yongle Li, Zijun Chen, Chongming Gao, Junqi Zhang, Yangyang Li, and Fuli Feng. 2024c · 2024
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Prompt engineering a prompt engineer
Qinyuan Ye, Maxamed Axmed, Reid Pryzant, and Fereshte Khani. 2024 · 2024
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Textgrad: Automatic "differentiation" via text
Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. 2024 · 2024
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Unveiling the lexical sensitivity of llms: Combinatorial optimization for prompt enhancement
Pengwei Zhan, Zhen Xu, Qian Tan, Jie Song, and Ru Xie. 2024 · 2024
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Crispo: Multi-aspect critique-suggestion-guided automatic prompt optimization for text generation
Han He, Qianchu Liu, Lei Xu, Chaitanya Shivade, Yi Zhang, Sundararajan Srinivasan, and Katrin Kirchhoff. 2025 · 2025
Closest in time.
FIPO: Free-form instruction-oriented prompt optimization with preference dataset and modular fine-tuning schema
Junru Lu, Siyu An, Min Zhang, Yulan He, Di Yin, and Xing Sun. 2025 · 2025
Closest in time.
Align-pro: A principled approach to prompt optimization for llm alignment
Prashant Trivedi, Souradip Chakraborty, Avinash Reddy, Vaneet Aggarwal, Amrit Singh Bedi, and George K. Atia. 2025 · 2025
Closest in time.
One prompt is not enough: automated construction of a mixture-of-expert prompts
Ruochen Wang, Sohyun An, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, and Cho-Jui Hsieh. 2025 · 2025
Closest in time.