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This paper investigates the rational thinking capability of Large Language Models (LLMs) in multi-round argumentative debates by exploring the impact of fallacious arguments on their logical reasoning performance.
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Habernal, I.; Wachsmuth, H.; Gurevych, I.; and Stein, B. 2018a · 1940
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How emotions affect logical reasoning: evidence from experiments with mood-manipulated participants, spider phobics, and people with exam anxiety
Jung, N.; Wranke, C.; Hamburger, K.; and Knauff, M. 2014 · 2014
Earlier work this paper cites.
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Habernal, I.; Wachsmuth, H.; Gurevych, I.; and Stein, B. 2018b · 2018
Earlier work this paper cites.
Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation
Habernal, I.; Wachsmuth, H.; Gurevych, I.; and Stein, B. 2018c · 2018
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Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
Wang, X.; Shi, W.; Kim, R.; Oh, Y.; Yang, S.; Zhang, J.; and Yu, Z. 2019 · 2019
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Defending against neural fake news
Zellers, R.; Holtzman, A.; Rashkin, H.; Bisk, Y.; Farhadi, A.; Roesner, F.; and Choi, Y. 2019 · 2019
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The limitations of stylometry for detecting machine-generated fake news
Schuster, T.; Schuster, R.; Shah, D. J.; and Barzilay, R. 2020 · 2020
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Characterizing the Online Discourse in Twitter: Users’ Reaction to Misinformation around COVID-19 in Twitter
Kalantari, N.; Liao, D.; and Motti, V. G. 2021 · 2021
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Gender and representation bias in GPT-3 generated stories
Lucy, L.; and Bamman, D. 2021 · 2021
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“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses
Sheng, E.; Chang, K.-W.; Natarajan, P.; and Peng, N. 2021 · 2021
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PaLM: Scaling Language Modeling with Pathways
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; Schuh, P.; Shi, K.; Tsvyashchenko, S.; Maynez, J.; Rao, A.; Barnes, P.; Tay, Y.; Shazeer, N.; Prabhakaran, V.; Reif, E.; Du, N.; Hutchinson, B.; Pope, R.; Bradbury, J.; Austin, J.; Isard, M.; Gur-Ari, G.; Yin, P.; Duke, T.; Levskaya, A.; Ghemawat, S.; Dev, S.; Michalewski, H.; Garcia, X.; Misra, V.; Robinson, K.; Fedus, L.; Zhou, D.; Ippolito, D.; Luan, D.; Lim, H.; Zoph, B.; Spiridonov, A.; Sepassi, R.; Dohan, D.; Agrawal, S.; Omernick, M.; Dai, A. M.; Pillai, T. S.; Pellat, M.; Lewkowycz, A.; Moreira, E.; Child, R.; Polozov, O.; Lee, K.; Zhou, Z.; Wang, X.; Saeta, B.; Diaz, M.; Firat, O.; Catasta, M.; Wei, J.; Meier-Hellstern, K.; Eck, D.; Dean, J.; Petrov, S.; and Fiedel, N. 2022 · 2022
Earlier work this paper cites.
Logical Fallacy Detection
Jin, Z.; Lalwani, A.; Vaidhya, T.; Shen, X.; Ding, Y.; Lyu, Z.; Sachan, M.; Mihalcea, R.; and Schoelkopf, B. 2022 · 2022
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Large Language Models are Zero-Shot Reasoners
Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022
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Solving Quantitative Reasoning Problems with Language Models
Lewkowycz, A.; Andreassen, A.; Dohan, D.; Dyer, E.; Michalewski, H.; Ramasesh, V.; Slone, A.; Anil, C.; Schlag, I.; Gutman-Solo, T.; Wu, Y.; Neyshabur, B.; Gur-Ari, G.; and Misra, V. 2022 · 2022
Cited alongside, same era.
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Lin, S.; Hilton, J.; and Evans, O. 2022 · 2022
Cited alongside, same era.
Red teaming language models with language models
Perez, E.; Huang, S.; Song, F.; Cai, T.; Ring, R.; Aslanides, J.; Glaese, A.; McAleese, N.; and Irving, G. 2022 · 2022
Better Language Models and Their Implications
OpenAI. 2023a · 2023
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Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
Pan, L.; Albalak, A.; Wang, X.; and Wang, W. Y. 2023 · 2023
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Thakur, V. 2023 · 2023
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Large Language Models Still Can’t Plan (A Benchmark for LLMs on Planning and Reasoning about Change)
Valmeekam, K.; Olmo, A.; Sreedharan, S.; and Kambhampati, S. 2023 · 2023
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Emergent analogical reasoning in large language models
Webb, T.; Holyoak, K. J.; and Lu, H. 2023 · 2023
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Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Srivastava, A.; Rastogi, A.; Rao, A.; Shoeb, A. A. M.; Abid, A.; Fisch, A.; Brown, A. R.; Santoro, A.; Gupta, A.; Garriga-Alonso, A.; et al. 2022 · 2022
Cited alongside, same era.
Chain of Thought Prompting Elicits Reasoning in Large Language Models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; brian ichter; Xia, F.; Chi, E. H.; Le, Q. V.; and Zhou, D. 2022 · 2022
Cited alongside, same era.
Bian, N.; Han, X.; Sun, L.; Lin, H.; Lu, Y.; and He, B. 2023 · 2023
Cited alongside, same era.
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Du, Y.; Li, S.; Torralba, A.; Tenenbaum, J. B.; and Mordatch, I. 2023 · 2023
Cited alongside, same era.
MathPrompter: Mathematical Reasoning using Large Language Models
Imani, S.; Du, L.; and Shrivastava, H. 2023 · 2023
Cited alongside, same era.
Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models’ Reasoning Performance
Fu, Y.; Ou, L.; Chen, M.; Wan, Y.; Peng, H.; and Khot, T. 2023a
Cited in the paper.
Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
Fu, Y.; Peng, H.; Khot, T.; and Lapata, M. 2023b
Cited in the paper.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Ichter, B.; Xia, F.; Chi, E.; Le, Q.; and Zhou, D. 2023 · 2023
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Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Xu, F.; Lin, Q.; Han, J.; Zhao, T.; Liu, J.; and Cambria, E. 2023 · 2023
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Coding Inequity: Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in Healthcare
Zack, T.; Lehman, E.; Suzgun, M.; Rodriguez, J. A.; Celi, L. A.; Gichoya, J.; Jurafsky, D.; Szolovits, P.; Bates, D. W.; Abdulnour, R.-E. E.; et al. 2023 · 2023
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A Survey of Large Language Models
Zhao, W. X.; Zhou, K.; Li, J.; Tang, T.; Wang, X.; Hou, Y.; Min, Y.; Zhang, B.; Zhang, J.; Dong, Z.; Du, Y.; Yang, C.; Chen, Y.; Chen, Z.; Jiang, J.; Ren, R.; Li, Y.; Tang, X.; Liu, Z.; Liu, P.; Nie, J.-Y.; and Wen, J.-R. 2023 · 2023
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Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Zhou, D.; Schärli, N.; Hou, L.; Wei, J.; Scales, N.; Wang, X.; Schuurmans, D.; Cui, C.; Bousquet, O.; Le, Q. V.; and Chi, E. H. 2023 · 2023
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