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Recent advancements in large language models (LLMs) revolutionize the field of intelligent agents, enabling collaborative multi-agent systems capable of tackling complex problems across various domains.
Asch, S.E.: Effects of group pressure upon the modification and distortion of judgments. In: Guetzkow, H. (ed.) Groups, leadership and men; research in human relations, pp. 177–190. Carnegie Press (1951)
1951
Earlier work this paper cites.
Asch, S.E.: Opinions and social pressure. Scientific American
1955
Earlier work this paper cites.
Asch, S.E.: Studies of independence and conformity: I. a minority of one against a unanimous majority. Psychological monographs: General and applied
1956
Earlier work this paper cites.
Kelman, H.C.: Compliance, identification, and internalization three processes of attitude change. Journal of conflict resolution
1958
Earlier work this paper cites.
Beloff, H.: Two forms of social conformity: acquiescence and conventionality. The Journal of Abnormal and Social Psychology
1958
Earlier work this paper cites.
Willis, R.H.: Conformity, independence, and anticonformity. Human Relations
1965
Earlier work this paper cites.
Pollis, N.P., Montgomery, R.L.: Conformity and resistance to compliance. The Journal of Psychology
1966
Earlier work this paper cites.
Zimbardo, P.G.: On the ethics of intervention in human psychological research: with special reference to the stanford prison experiment. Cognition (1973)
1973
Earlier work this paper cites.
Witkin, H.A., Price-Williams, D., Bertini, M., Christiansen, B., Oltman, P.K., Ramirez, M., Meel, J.V.: Social conformity and psychological differentiation. International Journal of Psychology
1974
Earlier work this paper cites.
Larsen, K.S.: The asch conformity experiment: Replication and transhistorical comparison. Journal of Social Behavior and Personality
1990
Earlier work this paper cites.
Baron, R.S., Vandello, J.A., Brunsman, B.: The forgotten variable in conformity research: Impact of task importance on social influence. Journal of personality and social psychology
1996
Earlier work this paper cites.
Turner, M.E., Pratkanis, A.R.: A social identity maintenance model of groupthink. Organizational behavior and human decision processes
1998
Earlier work this paper cites.
Turner, M.E., Pratkanis, A.R.: Twenty-five years of groupthink theory and research: Lessons from the evaluation of a theory. Organizational behavior and human decision processes
1998
Earlier work this paper cites.
Bond, R.: Group size and conformity. Group processes & intergroup relations
2005
Earlier work this paper cites.
Hodges, B.H., Geyer, A.L.: A nonconformist account of the asch experiments: Values, pragmatics, and moral dilemmas. Personality and Social Psychology Review
2006
Earlier work this paper cites.
Janis, I.L.: Groupthink. IEEE Engineering Management Review
2008
Earlier work this paper cites.
Smith, J.R., Louis, W.R.: Group norms and the attitude–behaviour relationship. Social and Personality Psychology Compass
2009
Earlier work this paper cites.
Raafat, R.M., Chater, N., Frith, C.: Herding in humans. Trends in cognitive sciences
2009
Earlier work this paper cites.
Song, G., Ma, Q., Wu, F., Li, L.: The psychological explanation of conformity. Social Behavior and Personality: an international journal
2012
Earlier work this paper cites.
Stallen, M., Smidts, A., Sanfey, A.G.: Peer influence: neural mechanisms underlying in-group conformity. Frontiers in human neuroscience
2013
Earlier work this paper cites.
Brandstetter, J., Rácz, P., Beckner, C., Sandoval, E.B., Hay, J., Bartneck, C.: A peer pressure experiment: Recreation of the asch conformity experiment with robots. In: IROS. pp. 1335–1340 (2014). https://doi.org/10.1109/IROS.2014.6942730
2014
Earlier work this paper cites.
Coultas, J.C., Van Leeuwen, E.J.: Conformity: Definitions, types, and evolutionary grounding. Evolutionary perspectives on social psychology pp. 189–202 (2015)
2015
Earlier work this paper cites.
Mattke, J., Maier, C., Reis, L., Weitzel, T.: Herd behavior in social media: The role of facebook likes, strength of ties, and expertise. Information & Management
2020
Earlier work this paper cites.
Gabriel, I.: Artificial intelligence, values, and alignment. Minds and machines
2020
Earlier work this paper cites.
Roberts, A., Raffel, C., Shazeer, N.: How much knowledge can you pack into the parameters of a language model? In: EMNLP. pp. 5418–5426 (2020)
2020
Earlier work this paper cites.
Jiang, Z., Xu, F.F., Araki, J., Neubig, G.: How can we know what language models know? Transactions of the Association for Computational Linguistics
2020
Earlier work this paper cites.
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al.: Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 21). pp. 2633–2650 (2021)
2021
Earlier work this paper cites.
Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liska, A., Terzi, T., Gimenez, M., de Masson d’Autume, C., Kocisky, T., Ruder, S., et al.: Mind the gap: Assessing temporal generalization in neural language models. In: NeurIPS. pp. 29348–29363 (2021)
2021
Earlier work this paper cites.
De Cao, N., Aziz, W., Titov, I.: Editing factual knowledge in language models. In: EMNLP. pp. 6491–6506 (2021)
2021
Earlier work this paper cites.
Elazar, Y., Kassner, N., Ravfogel, S., Ravichander, A., Hovy, E., Schütze, H., Goldberg, Y.: Measuring and improving consistency in pretrained language models. Transactions of the Association for Computational Linguistics
2021
Earlier work this paper cites.
Shuster, K., Poff, S., Chen, M., Kiela, D., Weston, J.: Retrieval augmentation reduces hallucination in conversation. In: EMNLP Findings. pp. 3784–3803 (2021)
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. In: NeurIPS. pp. 27730–27744 (2022)
2022
Earlier work this paper cites.
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Van Den Driessche, G.B., Lespiau, J.B., Damoc, B., Clark, A., et al.: Improving language models by retrieving from trillions of tokens. In: ICML. pp. 2206–2240 (2022)
2022
Earlier work this paper cites.
Zhong, Z., Lei, T., Chen, D.: Training language models with memory augmentation. In: EMNLP. pp. 5657–5673 (2022)
2022
Earlier work this paper cites.
Park, J.S., Popowski, L., Cai, C., Morris, M.R., Liang, P., Bernstein, M.S.: Social simulacra: Creating populated prototypes for social computing systems. In: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. pp. 1–18 (2022)
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
Manakul, P., Liusie, A., Gales, M.J.: Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. In: EMNLP. pp. 9004–9017 (2023)
2023
Cited alongside, same era.
Liu, B., Jiang, Y., Zhang, X., Liu, Q., Zhang, S., Biswas, J., Stone, P.: Llm+ p: Empowering large language models with optimal planning proficiency. In: NeurIPS (2023)
2023
Cited alongside, same era.
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., Grave, E.: Atlas: Few-shot learning with retrieval augmented language models. JMLR
2023
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., Yih, W.t.: Replug: Retrieval-augmented black-box language models. In: NAACL. pp. 8371–8384 (2024)
2024
Later among the works it cites.
Zhang, J., Xu, X., Deng, S.: Exploring collaboration mechanisms for llm agents: A social psychology view. In: ACL. pp. 14544–14607 (2024)
2024
Later among the works it cites.
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
Xiong, K., Ding, X., Cao, Y., Liu, T., Qin, B.: Examining inter-consistency of large language models collaboration: An in-depth analysis via debate. In: EMNLP Findings. pp. 7572–7590 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., Zhang, C.: Quantifying memorization across neural language models. In: ICLR (2023)
2023
Cited alongside, same era.
Baltaji, R., Hemmatian, B., Varshney, L.: Conformity, confabulation, and impersonation: Persona inconstancy in multi-agent LLM collaboration. In: Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP. pp. 17–31. Association for Computational Linguistics, Bangkok, Thailand (Aug 2024),
2024
Later among the works it cites.
Turpin, M., Michael, J., Perez, E., Bowman, S.: Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting. In: NeurIPS (2024)
2024
Later among the works it cites.
OpenAI: Hello gpt-4o
2024
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Meta: Introducing meta llama 3: The most capable openly available llm to date
2024
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Meta: Introducing llama 3.1: Our most capable models to date
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Gupta, S., Shrivastava, V., Deshpande, A., Kalyan, A., Clark, P., Sabharwal, A., Khot, T.: Bias Runs Deep: Implicit reasoning biases in persona-assigned LLMs. In: ICLR (2024)
2024
Later among the works it cites.
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., Yao, S.: Reflexion: Language agents with verbal reinforcement learning. In: NeurIPS (2024)
2024
Later among the works it cites.
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., Chen, W.: Critic: Large language models can self-correct with tool-interactive critiquing. In: ICLR (2024)
2024
Later among the works it cites.
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al.: Self-refine: Iterative refinement with self-feedback. In: NeurIPS (2024)
2024
Later among the works it cites.
Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y.N., Zhang, Y.Q., Xue, L., Shalev-Shwartz, S., et al.: Managing extreme ai risks amid rapid progress. Science
2024
Later among the works it cites.
2024
Later among the works it cites.
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language models can teach themselves to use tools. In: NeurIPS (2024)
2024
Later among the works it cites.
Xie, J., Zhang, K., Chen, J., Lou, R., Su, Y.: Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts. In: ICLR (2024),
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., Yih, S.W.t.: Trusting your evidence: Hallucinate less with context-aware decoding. In: NAACL. pp. 783–791 (2024)
2024
Later among the works it cites.
Li, N., Gao, C., Li, Y., Liao, Q.: EconAgent: Large language model-empowered agents for simulating macroeconomic activities. In: ACL. pp. 15523–15536 (2024)
2024
Later among the works it cites.
Zhao, Q., Wang, J., Zhang, Y., Jin, Y., Zhu, K., Chen, H., Xie, X.: Competeai: Understanding the competition dynamics of large language model-based agents. In: ICML (2024)
2024
Later among the works it cites.
2024
Later among the works it cites.
Gao, C., Xu, F., Chen, X., Wang, X., He, X., Li, Y.: Simulating human society with large language model agents: City, social media, and economic system. In: Companion Proceedings of the ACM on Web Conference 2024. pp. 1290–1293 (2024)
2024
Later among the works it cites.
Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. In: ICML (2024)
2024
Later among the works it cites.
Chan, C.M., Chen, W., Su, Y., Yu, J., Xue, W., Zhang, S., Fu, J., Liu, Z.: Chateval: Towards better llm-based evaluators through multi-agent debate. In: ICLR (2024)
2024
Later among the works it cites.
Tang, X., Zou, A., Zhang, Z., Zhao, Y., Zhang, X., Cohan, A., Gerstein, M.: Medagents: Large language models as collaborators for zero-shot medical reasoning. In: ACL Findings. pp. 599–621 (2024)
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., Liu, Z., Sun, M.: Communicative agents for software development. In: ACL. pp. 15174–15186 (2024)
2024
Later among the works it cites.
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A.H., White, R.W., Burger, D., Wang, C.: Autogen: Enabling next-gen llm applications via multi-agent conversation framework. In: COLM (2024)
2024
Later among the works it cites.
2024
Later among the works it cites.
Wang, Y., Ma, X., Zhang, G., Ni, Y., Chandra, A., Guo, S., Ren, W., Arulraj, A., He, X., Jiang, Z., et al.: Mmlu-pro: A more robust and challenging multi-task language understanding benchmark. In: NeurIPS (2024)
2024
Later among the works it cites.
Wang, W., Yang, Y., Pan, Y.: Visual knowledge in the big model era: Retrospect and prospect. Frontiers of Information Technology & Electronic Engineering
2025
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