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Recent advancements in large language models (LLMs) have shown remarkable progress, yet their ability to solve complex problems remains limited.
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Alan Mathison Turing et al · 1936
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Leopold Löwenheim · 1967
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Logic, Language, and Meaning, Volume 2: intensional logic and logical grammar
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Omar Zaidan, Jason Eisner, and Christine Piatko · 2007
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Thinking, fast and slow
Daniel Kahneman · 2011
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Proof in Alonzo Church’s and Alan Turing’s Mathematical Logic: Undecidability of First Order Logic
Jonathan Okeke Chimakonam · 2012
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Higher-order logical inference with compositional semantics
Koji Mineshima, Pascual Martínez-Gómez, Yusuke Miyao, and Daisuke Bekki · 2015
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Commonsense for generative multi-hop question answering tasks
Lisa Bauer, Yicheng Wang, and Mohit Bansal · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Exploiting explicit paths for multi-hop reading comprehension
Souvik Kundu, Tushar Khot, Ashish Sabharwal, and Peter Clark · 2018
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Todor Mihaylov and Anette Frank · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Cognitive graph for multi-hop reading comprehension at scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang · 2019
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Kagnet: Knowledge-aware graph networks for commonsense reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xiang Ren · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Improving natural language inference using external knowledge in the science questions domain
Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, et al · 2019
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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
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Scalable multi-hop relational reasoning for knowledge-aware question answering
Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, and Xiang Ren · 2020
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INFOTABS: inference on tables as semi-structured data
Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, and Vivek Srikumar · 2020
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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
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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
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Proofwriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark · 2020
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Peter Hase and Mohit Bansal · 2021
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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
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Show your work: Scratchpads for intermediate computation with language models
Pal: Program-aided language models
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Tora: A tool-integrated reasoning agent for mathematical problem solving
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Large language model guided tree-of-thought
Jieyi Long · 2023
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Microsoft guidance library
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Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
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Refining language models with compositional explanations
Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, and Xiang Ren · 2021
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Faithful reasoning using large language models
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Complexity-based prompting for multi-step reasoning
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot · 2022
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Folio: Natural language reasoning with first-order logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, et al · 2022
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Draft, sketch, and prove: Guiding formal theorem provers with informal proofs
Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, and Guillaume Lample · 2022
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Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
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OpenAI · 2023
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Llms cannot find reasoning errors, but can correct them!
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Progressive-hint prompting improves reasoning in large language models
Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li · 2023
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Confidence matters: Revisiting intrinsic self-correction capabilities of large language models
Loka Li, Guangyi Chen, Yusheng Su, Zhenhao Chen, Yixuan Zhang, Eric Xing, and Kun Zhang · 2024
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Criticbench: Benchmarking llms for critique-correct reasoning
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A theoretical understanding of self-correction through in-context alignment
Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, and Yisen Wang · 2024
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Meta prompting for ai systems
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Self-discover: Large language models self-compose reasoning structures
Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V Le, Ed H Chi, Denny Zhou, Swaroop Mishra, and Huaixiu Steven Zheng · 2024
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Multi-level recommendation reasoning over knowledge graphs with reinforcement learning
Xiting Wang, Kunpeng Liu, Dongjie Wang, Le Wu, Yanjie Fu, and Xing Xie · 2098
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