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In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples.
Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 1905
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1906
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Learning from mistakes is easier said than done: Group and organizational influences on the detection and correction of human error
Amy C Edmondson. 1996 · 1996
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Learning from mistakes
Dante R Chialvo and Per Bak. 1999 · 1999
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When will they ever learn? learning and teaching from mistakes in the clinical context
William Berman. 2006 · 2006
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Logiqa: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2020 · 2007
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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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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2018 · 2018
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2021
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Complexity-based prompting for multi-step reasoning
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. 2022 · 2022
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Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al. 2022 · 2022
Cited alongside, same era.
Teaching small language models to reason
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2022 · 2022
Cited alongside, same era.
Evaluating explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C Lipton, Graham Neubig, and William W Cohen. 2022 · 2022
Cited alongside, same era.
Limitations of language models in arithmetic and symbolic induction
Jing Qian, Hong Wang, Zekun Li, Shiyang Li, and Xifeng Yan. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Automatic prompt augmentation and selection with chain-of-thought from labeled data
KaShun Shum, Shizhe Diao, and Tong Zhang. 2023 · 2023
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Democratizing reasoning ability: Tailored learning from large language model
Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang, Minghui Song, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, et al. 2023 · 2023
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C-pack: Packaged resources to advance general chinese embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023
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Lpml: llm-prompting markup language for mathematical reasoning
Ryutaro Yamauchi, Sho Sonoda, Akiyoshi Sannai, and Wataru Kumagai. 2023 · 2023
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Cited alongside, same era.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022
Cited alongside, same era.
Learning from mistakes makes llm better reasoner
Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023
Cited alongside, same era.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023 · 2023
Cited alongside, same era.
Ning Bian, Xianpei Han, Le Sun, Hongyu Lin, Yaojie Lu, Ben He, Shanshan Jiang, and Bin Dong. 2023 · 2023
Cited alongside, same era.
Contrastive chain-of-thought prompting
Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, and Lidong Bing. 2023 · 2023
Cited alongside, same era.
Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023
Cited alongside, same era.
Mathprompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and Harsh Shrivastava. 2023 · 2023
Cited alongside, same era.
Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2023 · 2023
Later among the works it cites.
Lumos: Learning agents with unified data, modular design, and open-source llms
Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, and Bill Yuchen Lin. 2023 · 2023
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Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems
Dhara Yu, Noah D Goodman, and Jesse Mu. 2023 · 2023
Later among the works it cites.
Investigating symbolic capabilities of large language models
Neisarg Dave, Daniel Kifer, C Lee Giles, and Ankur Mali. 2024 · 2024
Closest in time.
Turning dust into gold: Distilling complex reasoning capabilities from llms by leveraging negative data
Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Bin Sun, Xinglin Wang, Heda Wang, and Kan Li. 2024 · 2024
Closest in time.
Autoact: Automatic agent learning from scratch via self-planning
Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Eleanor Jiang, Chengfei Lv, and Huajun Chen. 2024 · 2024
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2024 · 2024
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Can llms learn from previous mistakes? investigating llms’ errors to boost for reasoning
Yongqi Tong, Dawei Li, Sizhe Wang, Yujia Wang, Fei Teng, and Jingbo Shang. 2024 · 2024
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Evaluating mathematical reasoning beyond accuracy
Shijie Xia, Xuefeng Li, Yixin Liu, Tongshuang Wu, and Pengfei Liu. 2024 · 2024
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In-context principle learning from mistakes
Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng, Swaroop Mishra, Yiming Yang, Niket Tandon, and Uri Alon. 2024 · 2024
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Large language models as commonsense knowledge for large-scale task planning
Zirui Zhao, Wee Sun Lee, and David Hsu. 2024 · 2024
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