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Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems.
Science and Technology Advance through Surprise, January 2020
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Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
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Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Jonas, E. and Kording, K. P · 2017
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Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Alemayehu, C., Mitchell, G., and Nikles, J · 2018
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Arora, S., Li, Y., Liang, Y., Ma, T., and Risteski, A · 2018
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Bühlmann, P · 2018
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Fan, A., Lewis, M., and Dauphin, Y · 2018
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Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., and sayres, R · 2018
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Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
Gonen, H. and Goldberg, Y · 2019
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Rahimian, H. and Mehrotra, S · 2019
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Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé Iii, H., and Wallach, H · 2020
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The Curious Case of Neural Text Degeneration, April 2020
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
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An Interpretability Illusion for BERT, April 2021
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Abduction
Douven, I · 2021
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LoRA: Low-Rank Adaptation of Large Language Models, October 2021
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Towards causal representation learning, 2021
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y · 2021
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Advances in the agent-based modeling of economic and social behavior
Steinbacher, M., Raddant, M., Karimi, F., Camacho Cuena, E., Alfarano, S., Iori, G., and Lux, T · 2021
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SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
Abu Farha, I., Oprea, S. V., Wilson, S., and Magdy, W · 2022
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Artificial neural networks are making strides towards consciousness, 2022
Agüera y Arcas, B · 2022
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Constitutional AI: Harmlessness from AI Feedback, December 2022
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., Kerr, J., Mueller, J., Ladish, J., Landau, J., Ndousse, K., Lukosuite, K., Lovitt, L., Sellitto, M., Elhage, N., Schiefer, N., Mercado, N., DasSarma, N., Lasenby, R., Larson, R., Ringer, S., Johnston, S., Kravec, S., Showk, S. E., Fort, S., Lanham, T., Telleen-Lawton, T., Conerly, T., Henighan, T., Hume, T., Bowman, S. R., Hatfield-Dodds, Z., Mann, B., Amodei, D., Joseph, N., McCandlish, S., Brown, T., and Kaplan, J · 2022
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S · 2022
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Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty
Breznau, N., Rinke, E. M., Wuttke, A., Nguyen, H. H. V., Adem, M., Adriaans, J., Alvarez-Benjumea, A., Andersen, H. K., Auer, D., Azevedo, F., Bahnsen, O., Balzer, D., Bauer, G., Bauer, P. C., Baumann, M., Baute, S., Benoit, V., Bernauer, J., Berning, C., Berthold, A., Bethke, F. S., Biegert, T., Blinzler, K., Blumenberg, J. N., Bobzien, L., Bohman, A., Bol, T., Bostic, A., Brzozowska, Z., Burgdorf, K., Burger, K., Busch, K. B., Carlos-Castillo, J., Chan, N., Christmann, P., Connelly, R., Czymara, C. S., Damian, E., Ecker, A., Edelmann, A., Eger, M. A., Ellerbrock, S., Forke, A., Forster, A., Gaasendam, C., Gavras, K., Gayle, V., Gessler, T., Gnambs, T., Godefroidt, A., Grömping, M., Groß, M., Gruber, S., Gummer, T., Hadjar, A., Heisig, J. P., Hellmeier, S., Heyne, S., Hirsch, M., Hjerm, M., Hochman, O., Hövermann, A., Hunger, S., Hunkler, C., Huth, N., Ignácz, Z. S., Jacobs, L., Jacobsen, J., Jaeger, B., Jungkunz, S., Jungmann, N., Kauff, M., Kleinert, M., Klinger, J., Kolb, J.-P., Kołczyńska, M., Kuk, J., Kunißen, K., Kurti Sinatra, D., Langenkamp, A., Lersch, P. M., Löbel, L.-M., Lutscher, P., Mader, M., Madia, J. E., Malancu, N., Maldonado, L., Marahrens, H., Martin, N., Martinez, P., Mayerl, J., Mayorga, O. J., McManus, P., McWagner, K., Meeusen, C., Meierrieks, D., Mellon, J., Merhout, F., Merk, S., Meyer, D., Micheli, L., Mijs, J., Moya, C., Neunhoeffer, M., Nüst, D., Nygård, O., Ochsenfeld, F., Otte, G., Pechenkina, A. O., Prosser, C., Raes, L., Ralston, K., Ramos, M. R., Roets, A., Rogers, J., Ropers, G., Samuel, R., Sand, G., Schachter, A., Schaeffer, M., Schieferdecker, D., Schlueter, E., Schmidt, R., Schmidt, K. M., Schmidt-Catran, A., Schmiedeberg, C., Schneider, J., Schoonvelde, M., Schulte-Cloos, J., Schumann, S., Schunck, R., Schupp, J., Seuring, J., Silber, H., Sleegers, W., Sonntag, N., Staudt, A., Steiber, N., Steiner, N., Sternberg, S., Stiers, D., Stojmenovska, D., Storz, N., Striessnig, E., Stroppe, A.-K., Teltemann, J., Tibajev, A., Tung, B., Vagni, G., Van Assche, J., Van Der Linden, M., Van Der Noll, J., Van Hootegem, A., Vogtenhuber, S., Voicu, B., Wagemans, F., Wehl, N., Werner, H., Wiernik, B. M., Winter, F., Wolf, C., Yamada, Y., Zhang, N., Ziller, C., Zins, S., and Żółtak, T · 2022
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Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeover, July 2022
Cotra, A · 2022
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The Moral Consideration of Artificial Entities: A Literature Review
Harris, J. and Anthis, J. R · 2022
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Deep Learning Is Hitting a Wall - Nautilus, 2022
Marcus, G · 2022
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A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S. and Pitts, W · 2022
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Progress measures for grokking via mechanistic interpretability, September 2022
Nanda, N., Chan, L., Lieberum, T., Smith, J., and Steinhardt, J · 2022
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Methods of Coping with Social Desirability Bias: A Review
Nederhof, A. J · 2022
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Using large language models to simulate multiple humans and replicate human subject studies
Aher, G., Arriaga, R. I., and Kalai, A. T · 2023
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The Responsible Development of AI Agenda Needs to Include Consciousness Research, 2023
AMCS · 2023
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Out of One, Many: Using Language Models to Simulate Human Samples
Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., and Wingate, D · 2023
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Fairness and machine learning: limitations and opportunities
Barocas, S., Hardt, M., and Narayanan, A · 2023
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Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
Bender, E. M. and Koller, A · 2023
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Turning large language models into cognitive models, June 2023
Binz, M. and Schulz, E · 2023
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Sparks of Artificial General Intelligence: Early experiments with GPT-4, April 2023
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., and Zhang, Y · 2023
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Consciousness in Artificial Intelligence: Insights from the Science of Consciousness, August 2023
Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., Deane, G., Fleming, S. M., Frith, C., Ji, X., Kanai, R., Klein, C., Lindsay, G., Michel, M., Mudrik, L., Peters, M. A. K., Schwitzgebel, E., Simon, J., and VanRullen, R · 2023
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A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?
Chen, Y., Kirshner, S. N., Ovchinnikov, A., Andiappan, M., and Jenkin, T · 2023
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CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations
Cheng, M., Piccardi, T., and Yang, D · 2023
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The False Promise of ChatGPT
Chomsky, N., Roberts, I., and Watumull, J · 2023
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Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Chouldechova, A · 2023
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Should large language models replace human participants?
Crockett, M. and Messeri, L · 2023
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QLORA: efficient finetuning of quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
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Can AI language models replace human participants?
Dillion, D., Tandon, N., Gu, Y., and Gray, K · 2023
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The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
Gui, G. and Toubia, O · 2023
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In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models, May 2024
Kozlowski, A. C., Kwon, H., and Evans, J. A · 2024
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Language models, like humans, show content effects on reasoning tasks
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Lee, S., Yang, K.-Q., Peng, T.-Q., Heo, R., and Liu, H · 2024
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Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation, February 2024
Lum, K., Anthis, J. R., Nagpal, C., and D’Amour, A · 2024
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Sycophancy in Large Language Models: Causes and Mitigations, November 2024
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Holtzman, A., West, P., and Zettlemoyer, L · 2023
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Horton, J · 2023
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Hämäläinen, P., Tavast, M., and Kunnari, A · 2023
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Bank Run, Interrupted: Modeling Deposit Withdrawals with Generative AI, October 2023
Kazinnik, S · 2023
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Kenton, Z., Kumar, R., Farquhar, S., Richens, J., MacDermott, M., and Everitt, T · 2023
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Kim, S., Bae, S., Shin, J., Kang, S., Kwak, D., Yoo, K., and Seo, M · 2023
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Malmqvist, L · 2024
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Automated Social Science: Language Models as Scientist and Subjects, April 2024
Manning, B. S., Zhu, K., and Horton, J. J · 2024
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Virtual personas for language models via an anthology of backstories
Moon, S., Abdulhai, M., Kang, M., Suh, J., Soedarmadji, W., Behar, E. K., and Chan, D · 2024
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Position: Levels of AGI for operationalizing progress on the path to AGI
Morris, M. R., Sohl-Dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., and Legg, S · 2024
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Murthy, S. K., Ullman, T., and Hu, J · 2024
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Learning to Reason with LLMs, 2024
OpenAI · 2024
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Self-alignment of large language models via monopolylogue-based social scene simulation
Pang, X., Tang, S., Ye, R., Xiong, Y., Zhang, B., Wang, Y., and Chen, S · 2024
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AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories
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Petrov, N. B., Serapio-García, G., and Rentfrow, J · 2024
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Hidden Persuaders: LLMs’ Political Leaning and Their Influence on Voters
Potter, Y., Lai, S., Kim, J., Evans, J., and Song, D · 2024
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Performance and biases of Large Language Models in public opinion simulation
Qu, Y. and Wang, J · 2024
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Steering Llama 2 via Contrastive Activation Addition
Rimsky, N., Gabrieli, N., Schulz, J., Tong, M., Hubinger, E., and Turner, A · 2024
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Don’t ask if AI can make art — ask how AI can be art, September 2024
Robertson, A · 2024
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LLM Economicus? Mapping the Behavioral Biases of LLMs via Utility Theory, August 2024
Ross, J., Kim, Y., and Lo, A. W · 2024
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Large language models display human-like social desirability biases in Big Five personality surveys
Salecha, A., Ireland, M. E., Subrahmanya, S., Sedoc, J., Ungar, L. H., and Eichstaedt, J. C · 2024
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Salinas, A. and Morstatter, F · 2024
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Sarstedt, M., Adler, S. J., Rau, L., and Schmitt, B · 2024
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Si, C., Yang, D., and Hashimoto, T · 2024
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LLMs achieve adult human performance on higher-order theory of mind tasks, May 2024
Street, W., Siy, J. O., Keeling, G., Baranes, A., Barnett, B., McKibben, M., Kanyere, T., Lentz, A., Arcas, B. A. y., and Dunbar, R. I. M · 2024
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Tan, D. C. H., Chanin, D., Lynch, A., Paige, B., Kanoulas, D., Garriga-Alonso, A., and Kirk, R · 2024
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Systematic Biases in LLM Simulations of Debates, September 2024
Taubenfeld, A., Dover, Y., Reichart, R., and Goldstein, A · 2024
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Theorizing with Large Language Models
Tranchero, M., Brenninkmeijer, C.-F., Murugan, A., and Nagaraj, A · 2024
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Do large language models perform the way people expect? measuring the human generalization function
Vafa, K., Rambachan, A., and Mullainathan, S · 2024
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2024
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Can Large Language Model Agents Simulate Human Trust Behavior?, November 2024
Xie, C., Chen, C., Jia, F., Ye, Z., Lai, S., Shu, K., Gu, J., Bibi, A., Hu, Z., Jurgens, D., Evans, J., Torr, P., Ghanem, B., and Li, G · 2024
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