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Large Language Models (LLMs) frequently struggle with complex reasoning tasks, failing to construct logically sound steps towards the solution.
Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Earlier work this paper cites.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Dale Schuurmans, Claire Cui, Oliver Bousquet, Quoc Le, and Ed Chi. 2023 · 2021
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, et al. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Learning from mistakes makes llm better reasoner
Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023
Earlier work this paper cites.
Do you trust chatgpt? – perceived credibility of human and ai-generated content
Martin Huschens, Martin Briesch, Dominik Sobania, and Franz Rothlauf. 2023 · 2023
Earlier work this paper cites.
Chain of natural language inference for reducing large language model ungrounded hallucinations
Daren Lei, Yaxi Li, Mengya (Mia) Hu, Vincent Wang, Mingyu abd Yun, Emily Ching, and Eslam Kamal. 2023 · 2023
Cited alongside, same era.
Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev. 2023 · 2023
Cited alongside, same era.
Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. 2023 · 2023
Cited alongside, same era.
Llms cannot find reasoning errors, but can correct them!
Gladys Tyen, Hassan Mansoor, Victor Cărbune, Peter Chen, and Tony Mak. 2023 · 2023
Do large language models know what they don’t know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. 2023 · 2023
Later among the works it cites.
Peter Zhang. 2023 · 2023
Later among the works it cites.
Verify-and-edit: A knowledge-enhanced chain-of-thought framework
Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing. 2023 · 2023
Later among the works it cites.
Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2024 · 2024
Closest in time.
Codechain: Towards modular code generation through chain of self-revisions with representative sub-modules
Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo, and Shafiq Joty. 2024 · 2024
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Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Adaptation with self-evaluation to improve selective prediction in llms
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Tomas Pfister, and Somesh Jha. 2023a
Cited in the paper.
Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, and Ajay Divakaran. 2023b
Cited in the paper.