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Detecting cognitive biases in large language models (LLMs) is a fascinating task that aims to probe the existing cognitive biases within these models.
Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty
A. Tversky and D. Kahneman · 1974
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Judgment under uncertainty: Heuristics and biases
K. Daniel · 1982
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The curse of knowledge in economic settings: An experimental analysis
C. Camerer, G. Loewenstein, and M. Weber · 1989
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Survivorship bias in performance studies
S. J. Brown, W. Goetzmann, R. G. Ibbotson, and S. A. Ross · 1992
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Risk compensation—the case of road lighting
T. Assum, T. Bjørnskau, S. Fosser, and F. Sagberg · 1999
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Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments
J. Kruger and D. Dunning · 1999
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The ikea effect: When labor leads to love
M. I. Norton, D. Mochon, and D. Ariely · 2012
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Prospect theory: An analysis of decision under risk
D. Kahneman and A. Tversky · 2013
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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The impact bias is alive and well
T. D. Wilson and D. T. Gilbert · 2013
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Towards a taxonomy of conversational discourse types: An empirical corpus-based analysis
D. Biber, J. Egbert, D. Keller, and S. Wizner · 2021
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Calibrate before use: Improving few-shot performance of language models
Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh · 2021
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Large language models are few-shot clinical information extractors
M. Agrawal, S. Hegselmann, H. Lang, Y. Kim, and D. Sontag · 2022
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Capturing failures of large language models via human cognitive biases
E. Jones and J. Steinhardt · 2022
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Chain-of-thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al · 2022
Cited alongside, same era.
Using large language models to simulate multiple humans and replicate human subject studies
G. V. Aher, R. I. Arriaga, and A. T. Kalai · 2023
Cited alongside, same era.
Cognitive biases in natural language: Automatically detecting, differentiating, and measuring bias in text
K. Atreides and D. J. Kelley · 2023
Cited alongside, same era.
Thinking and deciding
J. Baron · 2023
Cited alongside, same era.
Improving factuality and reasoning in language models through multiagent debate
Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch · 2023
Cited alongside, same era.
Avalon’s game of thoughts: Battle against deception through recursive contemplation
S. Wang, C. Liu, Z. Zheng, S. Qi, S. Chen, Q. Yang, A. Zhao, C. Wang, S. Song, and G. Huang · 2023
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Bloomberggpt: A large language model for finance
S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, and G. Mann · 2023
Later among the works it cites.
Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors
W. Chen, Y. Su, J. Zuo, C. Yang, C. Yuan, C.-M. Chan, H. Yu, Y. Lu, Y.-H. Hung, C. Qian, Y. Qin, X. Cong, R. Xie, Z. Liu, M. Sun, and J. Zhou · 2024
Closest in time.
Cognitive bias in high-stakes decision-making with llms, 2024
J. Echterhoff, Y. Liu, A. Alessa, J. McAuley, and Z. He · 2024
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MetaGPT: Meta programming for a multi-agent collaborative framework
S. Hong, M. Zhuge, J. Chen, X. Zheng, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou, C. Ran, L. Xiao, C. Wu, and J. Schmidhuber · 2024
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S. Jinxin, Z. Jiabao, W. Yilei, W. Xingjiao, L. Jiawen, and H. Liang · 2023
Cited alongside, same era.
Mind the biases: Quantifying cognitive biases in language model prompting
R. Lin and H. T. Ng · 2023
Cited alongside, same era.
Can generalist foundation models outcompete special-purpose tuning? case study in medicine
H. Nori, Y. T. Lee, S. Zhang, D. Carignan, R. Edgar, N. Fusi, N. King, J. Larson, Y. Li, W. Liu, et al · 2023
Cited alongside, same era.
Generative agents: Interactive simulacra of human behavior
J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
Cited alongside, same era.
How well does gpt phish people? an investigation involving cognitive biases and feedback
M. Sharma, K. Singh, P. Aggarwal, and V. Dutt · 2023
Cited alongside, same era.
Large language models encode clinical knowledge
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl, et al · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, et al · 2023
Cited alongside, same era.
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Instructed to bias: Instruction-tuned language models exhibit emergent cognitive bias
I. Itzhak, G. Stanovsky, N. Rosenfeld, and Y. Belinkov · 2024
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Benchmarking cognitive biases in large language models as evaluators
R. Koo, M. Lee, V. Raheja, J. I. Park, Z. M. Kim, and D. Kang · 2024
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(ir) rationality and cognitive biases in large language models
O. Macmillan-Scott and M. Musolesi · 2024
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Experiential co-learning of software-developing agents
C. Qian, Y. Dang, J. Li, W. Liu, Z. Xie, Y. Wang, W. Chen, C. Yang, X. Cong, X. Che, Z. Liu, and M. Sun · 2024
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Addressing cognitive bias in medical language models
S. Schmidgall, C. Harris, I. Essien, D. Olshvang, T. Rahman, J. W. Kim, R. Ziaei, J. Eshraghian, P. Abadir, and R. Chellappa · 2024
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Autogen: Enabling next-gen LLM applications via multi-agent conversation
Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang · 2024
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Doctor versus artificial intelligence: Patient and physician evaluation of large language model responses to rheumatology patient questions in a cross-sectional study
C. Ye, E. Zweck, Z. Ma, J. Smith, and S. Katz · 2024
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Building cooperative embodied agents modularly with large language models
H. Zhang, W. Du, J. Shan, Q. Zhou, Y. Du, J. B. Tenenbaum, T. Shu, and C. Gan · 2024
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