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With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities.
The logic theory machine–a complex information processing system
A. Newell and H. Simon · 1956
Earlier work this paper cites.
Programs with common sense
John McCarthy · 1959
Earlier work this paper cites.
Some philosophical problems from the standpoint of artificial intelligence
J. McCarthy and P.J. Hayes · 1981
Earlier work this paper cites.
Logic for natural language analysis
Fernando Carlos Neves Pereira · 1982
Earlier work this paper cites.
Artificial intelligence, logic and formalizing common sense
John McCarthy · 1989
Earlier work this paper cites.
Formal semantics: an introduction
Ronnie Cann · 1993
Earlier work this paper cites.
Deeplogic: Towards end-to-end differentiable logical reasoning, 2019
Nuri Cingillioglu and Alessandra Russo · 2019
Earlier work this paper cites.
Clutrr: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton · 2019
Earlier work this paper cites.
Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver, 2019
Po-Wei Wang, Priya L. Donti, Bryan Wilder, and Zico Kolter · 2019
Earlier work this paper cites.
Logical inferences with comparatives and generalized quantifiers
Izumi Haruta, Koji Mineshima, and Daisuke Bekki · 2020
Earlier work this paper cites.
Reclor: A reading comprehension dataset requiring logical reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng · 2020
Earlier work this paper cites.
Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, et al · 2021
Earlier work this paper cites.
Natural language inference in context - investigating contextual reasoning over long texts
Hanmeng Liu, Leyang Cui, Jian Liu, and Yue Zhang · 2021
Earlier work this paper cites.
Logiqa: a challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2021
Earlier work this paper cites.
NeuroLogic decoding: (un)supervised neural text generation with predicate logic constraints
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
Earlier work this paper cites.
Neural natural logic inference for interpretable question answering
Jihao Shi, Xiao Ding, Li Du, Ting Liu, and Bing Qin · 2021
Earlier work this paper cites.
ProofWriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark · 2021
Earlier work this paper cites.
Diagnosing the first-order logical reasoning ability through LogicNLI
Jidong Tian, Yitian Li, Wenqing Chen, Liqiang Xiao, Hao He, and Yaohui Jin · 2021
Earlier work this paper cites.
Logitorch: A pytorch-based library for logical reasoning on natural language
Chadi Helwe, Chloé Clavel, and Fabian Suchanek · 2022
Earlier work this paper cites.
Maieutic prompting: Logically consistent reasoning with recursive explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, et al · 2022
Earlier work this paper cites.
AnaLog: Testing analytical and deductive logic learnability in language models
Samuel Ryb, Mario Giulianelli, Arabella Sinclair, and Raquel Fernández · 2022
Earlier work this paper cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, et al · 2022
Earlier work this paper cites.
Logical reasoning with span-level predictions for interpretable and robust NLI models
Joe Stacey, Pasquale Minervini, Haim Dubossarsky, and Marek Rei · 2022
Earlier work this paper cites.
From lsat: The progress and challenges of complex reasoning
Siyuan Wang, Zhongkun Liu, Wanjun Zhong, Ming Zhou, Zhongyu Wei, et al · 2022
Earlier work this paper cites.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins · 2023
Earlier work this paper cites.
True detective: A deep abductive reasoning benchmark undoable for GPT-3 and challenging for GPT-4
Maksym Del and Mark Fishel · 2023
Earlier work this paper cites.
Boosting logical reasoning in large language models through a new framework: The graph of thought
Bin Lei, Chunhua Liao, Caiwen Ding, et al · 2023
Earlier work this paper cites.
Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding
Hanmeng Liu, Jian Liu, Leyang Cui, Zhiyang Teng, Nan Duan, et al · 2023
Earlier work this paper cites.
Evaluating the logical reasoning ability of chatgpt and gpt-4, 2023
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Man Luo, Shrinidhi Kumbhar, Ming shen, Mihir Parmar, Neeraj Varshney, et al · 2024
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Exploring the role of reasoning structures for constructing proofs in multi-step natural language reasoning with large language models
Christopher Malon, Martin Min, Xiaodan Zhu, et al · 2024
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Enhancing reasoning capabilities of llms via principled synthetic logic corpus
Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, and Yasuhiro Sogawa · 2024
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Learning to reason with LLMs
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Logicbench: Towards systematic evaluation of logical reasoning ability of large language models
Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, et al · 2024
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Hyun Ryu, Gyeongman Kim, Hyemin S Lee, and Eunho Yang · 2024
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Chain of logic: Rule-based reasoning with large language models
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Chenxu Wang, Ping Jian, and Zhen Yang · 2024
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Training large language models for reasoning through reverse curriculum reinforcement learning
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Symbol-LLM: Towards foundational symbol-centric interface for large language models
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Aristotle: Mastering logical reasoning with a logic-complete decompose-search-resolve framework
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Faithful logical reasoning via symbolic chain-of-thought
Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, and Wynne Hsu · 2024
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Harnessing the power of large language models for natural language to first-order logic translation
Yuan Yang, Siheng Xiong, Ali Payani, Ehsan Shareghi, and Faramarz Fekri · 2024
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Language models as inductive reasoners
Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, et al · 2024
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Natural language reasoning, a survey
Fei Yu, Hongbo Zhang, Prayag Tiwari, and Benyou Wang · 2024
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Formal language knowledge corpus for retrieval augmented generation, 2024
Majd Zayyad and Yossi Adi · 2024
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o1-coder: an o1 replication for coding
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Marco-o1: Towards open reasoning models for open-ended solutions
Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, et al · 2024
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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI · 2025
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