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Exploring the capabilities of Large Language Models (LLMs) in puzzle solving unveils critical insights into their potential and challenges in AI, marking a significant step towards understanding their applicability in complex reasoning tasks.
Roberta: A robustly optimized bert pretraining approach
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
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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 · 1911
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Finding optimal solutions to rubik’s cube using pattern databases
Richard E. Korf. 1997 · 1997
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Abductive and inductive reasoning: background and issues
Peter A. Flach and Antonis C. Kakas. 2000 · 2000
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Minesweeper as a constraint satisfaction problem
Chris Studholme. 2001 · 2001
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Deep blue
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Sudoku as a constraint problem
Helmut Simonis. 2005 · 2005
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Techniques for solving sudoku puzzles
Eric C. Chi and Kenneth Lange. 2013 · 2013
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Learning to automatically solve logic grid puzzles
Arindam Mitra and Chitta Baral. 2015 · 2015
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. 2017 · 2017
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Solving the rubik’s cube without human knowledge
Stephen McAleer, Forest Agostinelli, Alexander Shmakov, and Pierre Baldi. 2018 · 2018
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Solving the rubik’s cube with deep reinforcement learning and search
Forest Agostinelli, Stephen Marcus McAleer, Alexander Shmakov, and Pierre Baldi. 2019 · 2019
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdel rahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 2019
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Unifiedqa: Crossing format boundaries with a single qa system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, David W. Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William H. Guss, Alex Nichol, Igor Babuschkin, Suchir Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew M. Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
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Cryptonite: A cryptic crossword benchmark for extreme ambiguity in language
Avia Efrat, Uri Shaham, Dan Kilman, and Omer Levy. 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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Riddlesense: Reasoning about riddle questions featuring linguistic creativity and commonsense knowledge
Bill Yuchen Lin, Ziyi Wu, Yichi Yang, Dong-Ho Lee, and Xiang Ren. 2021 · 2021
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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Puzzle solving without search or human knowledge: An unnatural language approach
David A. Noever and Ryerson Burdick. 2021 · 2021
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Decrypting cryptic crosswords: Semantically complex wordplay puzzles as a target for nlp
Josh Rozner, Christopher Potts, and Kyle Mahowald. 2021 · 2021
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Tal Schuster, A. Kalyan, Oleksandr Polozov, and Adam Tauman Kalai. 2021 · 2021
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A puzzle-based dataset for natural language inference
Roxana Szomiu and Adrian Groza. 2021 · 2021
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Birdqa: A bilingual dataset for question answering on tricky riddles
Yunxiang Zhang and Xiaojun Wan. 2021 · 2021
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Different algorithms to solve a rubik’s cube
Juntao Chen. 2022 · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, S. Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Wei Yu, Vincent Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed Huai hsin Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Go beyond the obvious: Probing the gap of informal reasoning ability between humanity and llms by detective reasoning puzzle benchmark
Zhouhong Gu, Zihan Li, Lin Zhang, Zhuozhi Xiong, Sihang Jiang, Xiaoxuan Zhu, Shusen Wang, Zili Wang, Jianchen Wang, Haoning Ye, Wenhao Huang, Yikai Zhang, Hongwei Feng, and Yanghua Xiao. 2023 · 2023
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Suspicion-agent: Playing imperfect information games with theory of mind aware gpt-4
Jiaxian Guo, Bo Yang, Paul Yoo, Bill Yuchen Lin, Yusuke Iwasawa, and Yutaka Matsuo. 2023 · 2023
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Are chatgpt and gpt-4 good poker players? - a pre-flop analysis
Akshat Gupta. 2023 · 2023
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Leveraging large language models to generate answer set programs
Adam Ishay, Zhun Yang, and Joohyung Lee. 2023 · 2023
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Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2022 · 2022
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True detective: A deep abductive reasoning benchmark undoable for gpt-3 and challenging for gpt-4
Maksym Del and Mark Fishel. 2022 · 2022
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Complexity-based prompting for multi-step reasoning
Yao Fu, Hao-Chun Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. 2022 · 2022
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Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2022 · 2022
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 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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Down and across: Introducing crossword-solving as a new nlp benchmark
Saurabh Kulshreshtha, Olga Kovaleva, Namrata Shivagunde, and Anna Rumshisky. 2022 · 2022
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Yifan Jiang, Filip Ilievski, and Kaixin Ma. 2023 · 2023
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Boardgameqa: A dataset for natural language reasoning with contradictory information
Mehran Kazemi, Quan Yuan, Deepti Bhatia, Najoung Kim, Xin Xu, Vaiva Imbrasaite, and Deepak Ramachandran. 2023 · 2023
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Llm-based agent society investigation: Collaboration and confrontation in avalon gameplay
Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, De-Yong Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, and Hao Wang. 2023 · 2023
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Boosting logical reasoning in large language models through a new framework: The graph of thought
Bin Lei, Pei-Hung Lin, Chunhua Liao, and Caiwen Ding. 2023 · 2023
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Assessing logical puzzle solving in large language models: Insights from a minesweeper case study
Yinghao Li, Haorui Wang, and Chao Zhang. 2023 · 2023
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Large language model guided tree-of-thought
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Man Luo, Shrinidhi Kumbhar, Ming shen, Mihir Parmar, Neeraj Varshney, Pratyay Banerjee, Somak Aditya, and Chitta Baral. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
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Tree of uncertain thoughts reasoning for large language models
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Linc: A neurosymbolic approach for logical reasoning by combining language models with first-order logic provers
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