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We introduce the framework of "social learning" in the context of large language models (LLMs), whereby models share knowledge with each other in a privacy-aware manner using natural language.
Social learning theory , volume 1
Bandura, A. and Walters, R. H · 1977
Earlier work this paper cites.
Learning in multi-agent systems
Alonso, E., D’Inverno, M., Kudenko, D., Luck, M., and Noble, J · 2001
Earlier work this paper cites.
Contributions to the study of sms spam filtering: new collection and results
Almeida, T. A., Hidalgo, J. M. G., and Yamakami, A · 2011
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
Earlier work this paper cites.
The lambada dataset: Word prediction requiring a broad discourse context
Paperno, D., Kruszewski, G., Lazaridou, A., Pham, Q. N., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., and Fernández, R · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
Adaptive federated optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., and McMahan, H. B · 2020
Earlier work this paper cites.
Us top 1000 baby names 1880-2020, Oct 2021
Hugequiz.com · 2021
Earlier work this paper cites.
Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2021
Earlier work this paper cites.
Cross-task generalization via natural language crowdsourcing instructions
Mishra, S., Khashabi, D., Baral, C., and Hajishirzi, H · 2021
Earlier work this paper cites.
Emergent Social Learning via Multi-agent Reinforcement Learning
Ndousse, K. K., Eck, D., Levine, S., and Jaques, N · 2021
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al · 2021
Cited alongside, same era.
Understanding unintended memorization in language models under federated learning
Thakkar, O. D., Ramaswamy, S., Mathews, R., and Beaufays, F · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
Cited alongside, same era.
PaLM: Scaling Language Modeling with Pathways, October 2022
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
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
Closest in time.
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Deng, Y., Zhang, W., Chen, Z., and Gu, Q · 2023
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Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I · 2023
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Distributed differential privacy for federated learning, 2023
Hartmann, F. and Kairouz, P · 2023
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Encouraging divergent thinking in large language models through multi-agent debate
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Cited alongside, same era.
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Huang, W., Abbeel, P., Pathak, D., and Mordatch, I · 2022
Cited alongside, same era.
Mind’s eye: Grounded language model reasoning through simulation, 2022
Liu, R., Wei, J., Gu, S. S., Wu, T.-Y., Vosoughi, S., Cui, C., Zhou, D., and Dai, A. M · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., and Zettlemoyer, L · 2022
Cited alongside, same era.
TALM: Tool Augmented Language Models, May 2022
Parisi, A., Zhao, Y., and Fiedel, N · 2022
Cited alongside, same era.
Red Teaming Language Models with Language Models, February 2022
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., and Irving, G · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Tu, Z., and Shi, S · 2023
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BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents, August 2023
Liu, Z., Yao, W., Zhang, J., Xue, L., Heinecke, S., Murthy, R., Feng, Y., Chen, Z., Niebles, J. C., Arpit, D., Xu, R., Mui, P., Wang, H., Xiong, C., and Savarese, S · 2023
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Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C · 2023
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Synthetic prompting: Generating chain-of-thought demonstrations for large language models
Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., and Chen, W · 2023
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A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al · 2023
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2023
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React: Synergizing reasoning and acting in language models, 2023
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2023
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Zamani, H., Trippas, J. R., Dalton, J., Radlinski, F., et al · 2023
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