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Recent studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse.
What computers can’t do: The limits of artificial intelligence
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Back to the future: Towards explainable temporal reasoning with large language models
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Scaling laws for neural language models
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Language models are few-shot learners
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Who thinks about the competition? managerial ability and strategic entry in us local telephone markets
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The 11–20 money request game: A level-k reasoning study
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Structural models of nonequilibrium strategic thinking: Theory, evidence, and applications
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Universal language model fine-tuning for text classification
Howard, Jeremy, Sebastian Ruder. 2018 · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, Nicholas, Chang Liu, Úlfar Erlingsson, Jernej Kos, Dawn Song. 2019 · 2019
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020
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Extracting training data from large language models
Carlini, Nicholas, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021 · 2021
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Understanding unintended memorization in language models under federated learning
Thakkar, Om Dipakbhai, Swaroop Ramaswamy, Rajiv Mathews, Francoise Beaufays. 2021 · 2021
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Rethinking the role of demonstrations: What makes in-context learning work?
Min, Sewon, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, Jason, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Achiam, Josh, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Using large language models to simulate multiple humans and replicate human subject studies
Aher, Gati V, Rosa I Arriaga, Adam Tauman Kalai. 2023 · 2023
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Out of one, many: Using language models to simulate human samples
Argyle, Lisa P, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, David Wingate. 2023 · 2023
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A systematic evaluation of large language models on out-of-distribution logical reasoning tasks
Bao, Qiming, Gaël Gendron, Alex Yuxuan Peng, Wanjun Zhong, Neset Tan, Yang Chen, Michael Witbrock, Jiamou Liu. 2023 · 2023
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The reversal curse: Llms trained on" a is b" fail to learn" b is a"
Berglund, Lukas, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, Owain Evans. 2023 · 2023
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Using gpt for market research
Brand, James, Ayelet Israeli, Donald Ngwe. 2023 · 2023
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Playing games with gpt: What can we learn about a large language model from canonical strategic games?
Brookins, Philip, Jason Matthew DeBacker. 2023 · 2023
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Rethink reporting of evaluation results in ai
Burnell, Ryan, Wout Schellaert, John Burden, Tomer D Ullman, Fernando Martinez-Plumed, Joshua B Tenenbaum, Danaja Rutar, Lucy G Cheke, Jascha Sohl-Dickstein, Melanie Mitchell, et al. 2023 · 2023
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Consciousness in artificial intelligence: insights from the science of consciousness
Butlin, Patrick, Robert Long, Eric Elmoznino, Yoshua Bengio, Jonathan Birch, Axel Constant, George Deane, Stephen M Fleming, Chris Frith, Xu Ji, et al. 2023 · 2023
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Open problems and fundamental limitations of reinforcement learning from human feedback
Casper, Stephen, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al. 2023 · 2023
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Llms differ from human cognition because they are not embodied
Chemero, Anthony. 2023 · 2023
Cited alongside, same era.
Can ai language models replace human participants?
Dillion, Danica, Niket Tandon, Yuling Gu, Kurt Gray. 2023 · 2023
Cited alongside, same era.
Large language models are not strong abstract reasoners
Gendron, Gaël, Qiming Bao, Michael Witbrock, Gillian Dobbie. 2023 · 2023
Cited alongside, same era.
Ai and the transformation of social science research
Grossmann, Igor, Matthew Feinberg, Dawn C Parker, Nicholas A Christakis, Philip E Tetlock, William A Cunningham. 2023 · 2023
Cited alongside, same era.
Hagendorff, Thilo. 2023 · 2023
Cited alongside, same era.
Dong, Yihong, Xue Jiang, Huanyu Liu, Zhi Jin, Ge Li. 2024 · 2024
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Frontiers: Can large language models capture human preferences?
Goli, Ali, Amandeep Singh. 2024 · 2024
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Economics arena for large language models
Guo, Shangmin, Haoran Bu, Haochuan Wang, Yi Ren, Dianbo Sui, Yuming Shang, Siting Lu. 2024 · 2024
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Changing answer order can decrease mmlu accuracy
Gupta, Vipul, David Pantoja, Candace Ross, Adina Williams, Megan Ung. 2024 · 2024
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How does chatgpt’think’? psychology and neuroscience crack open ai large language models
Hutson, Matthew. 2024 · 2024
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Hartmann, Jochen, Jasper Schwenzow, Maximilian Witte. 2023 · 2023
Cited alongside, same era.
Large language models as simulated economic agents: What can we learn from homo silicus?
Horton, John J. 2023 · 2023
Cited alongside, same era.
Huang, Lei, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2023 · 2023
Cited alongside, same era.
Jiang, Hang, Xiajie Zhang, Xubo Cao, Jad Kabbara. 2023 · 2023
Cited alongside, same era.
Swe-bench: Can language models resolve real-world github issues?
Jimenez, Carlos E, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik Narasimhan. 2023 · 2023
Cited alongside, same era.
Can large language models infer causation from correlation?
Jin, Zhijing, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab, Bernhard Schölkopf. 2023 · 2023
Cited alongside, same era.
Understanding the effects of rlhf on llm generalisation and diversity
Kirk, Robert, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, Roberta Raileanu. 2023 · 2023
Cited alongside, same era.
A survey on large language models for code generation
Jiang, Juyong, Fan Wang, Jiasi Shen, Sungju Kim, Sunghun Kim. 2024 · 2024
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Lewis, Martha, Melanie Mitchell. 2024 · 2024
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Frontiers: Determining the validity of large language models for automated perceptual analysis
Li, Peiyao, Noah Castelo, Zsolt Katona, Miklos Sarvary. 2024 · 2024
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Strategic interactions between large language models-based agents in beauty contests
Lu, Siting. 2024 · 2024
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Artificial intelligence and illusions of understanding in scientific research
Messeri, Lisa, MJ Crockett. 2024 · 2024
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Introducing meta llama 3: The most capable openly available llm to date
Meta, AI. 2024 · 2024
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Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models
Mirzadeh, Iman, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, Mehrdad Farajtabar. 2024 · 2024
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Nezhurina, Marianna, Lucia Cipolina-Kun, Mehdi Cherti, Jenia Jitsev. 2024 · 2024
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Towards systematic evaluation of logical reasoning ability of large language models
Parmar, Mihir, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral. 2024 · 2024
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Let’s think dot by dot: Hidden computation in transformer language models
Pfau, Jacob, William Merrill, Samuel R Bowman. 2024 · 2024
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Testing the general deductive reasoning capacity of large language models using ood examples
Saparov, Abulhair, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Mehran Kazemi, Najoung Kim, He He. 2024 · 2024
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Testing theory of mind in large language models and humans
Strachan, James WA, Dalila Albergo, Giulia Borghini, Oriana Pansardi, Eugenio Scaliti, Saurabh Gupta, Krati Saxena, Alessandro Rufo, Stefano Panzeri, Guido Manzi, et al. 2024 · 2024
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Llms achieve adult human performance on higher-order theory of mind tasks
Street, Winnie, John Oliver Siy, Geoff Keeling, Adrien Baranes, Benjamin Barnett, Michael McKibben, Tatenda Kanyere, Alison Lentz, Robin IM Dunbar, et al. 2024 · 2024
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Theorizing with large language models
Tranchero, Matteo, Cecil-Francis Brenninkmeijer, Arul Murugan, Abhishek Nagaraj. 2024 · 2024
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Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting
Turpin, Miles, Julian Michael, Ethan Perez, Samuel Bowman. 2024 · 2024
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Do large language models perform the way people expect? measuring the human generalization function
Vafa, Keyon, Ashesh Rambachan, Sendhil Mullainathan. 2024 · 2024
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Easy problems that llms get wrong
Williams, Sean, James Huckle. 2024 · 2024
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Language models meet world models: Embodied experiences enhance language models
Xiang, Jiannan, Tianhua Tao, Yi Gu, Tianmin Shu, Zirui Wang, Zichao Yang, Zhiting Hu. 2024 · 2024
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Hallucination is inevitable: An innate limitation of large language models
Xu, Ziwei, Sanjay Jain, Mohan Kankanhalli. 2024 · 2024
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Can large language models always solve easy problems if they can solve harder ones?
Yang, Zhe, Yichang Zhang, Tianyu Liu, Jian Yang, Junyang Lin, Chang Zhou, Zhifang Sui. 2024 · 2024
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Larger and more instructable language models become less reliable
Zhou, Lexin, Wout Schellaert, Fernando Martínez-Plumed, Yael Moros-Daval, Cèsar Ferri, José Hernández-Orallo. 2024 · 2024
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Experimenter demand effects in economic experiments
Zizzo, Daniel John. 2010 · 2024
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