Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities

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O. Levy, M. Seo, E. Choi, and L. Zettlemoyer, “Zero-shot relation extraction via reading comprehension,” in Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada, August 3-4, 2017
2017
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al
2019
Earlier work this paper cites.
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. F. Christiano, and G. Irving, “Fine-tuning language models from human preferences,” CoRR
Original
2019
Earlier work this paper cites.
F. Petroni, T. Rocktäschel, S. Riedel, P. S. H. Lewis, A. Bakhtin, Y. Wu, and A. H. Miller, “Language models as knowledge bases?,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019
2019
Earlier work this paper cites.
N. Stiennon, L. Ouyang, J. Wu, D. M. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano, “Learning to summarize with human feedback,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Earlier work this paper cites.
A. Roberts, C. Raffel, and N. Shazeer, “How much knowledge can you pack into the parameters of a language model?,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021
2021
Earlier work this paper cites.
M. Geva, R. Schuster, J. Berant, and O. Levy, “Transformer feed-forward layers are key-value memories,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021
2021
Earlier work this paper cites.
N. D. Cao, W. Aziz, and I. Titov, “Editing factual knowledge in language models,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” in The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022
2022
Earlier work this paper cites.
K. Meng, D. Bau, A. Andonian, and Y. Belinkov, “Locating and editing factual associations in GPT,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022
2022
Earlier work this paper cites.
S. S. Raman, V. Cohen, E. Rosen, I. Idrees, D. Paulius, and S. Tellex, “Planning with large language models via corrective re-prompting,” CoRR
Original
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. F. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022
2022
Earlier work this paper cites.
D. Dai, L. Dong, Y. Hao, Z. Sui, B. Chang, and F. Wei, “Knowledge neurons in pretrained transformers,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022
2022
Earlier work this paper cites.
H. Chase, “LangChain,” Oct. 2022
2022
Earlier work this paper cites.
A. Madaan, N. Tandon, P. Clark, and Y. Yang, “Memory-assisted prompt editing to improve GPT-3 after deployment,” CoRR
Original
2022
Earlier work this paper cites.
K. Luu, D. Khashabi, S. Gururangan, K. Mandyam, and N. A. Smith, “Time waits for no one! analysis and challenges of temporal misalignment,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022
2022
Earlier work this paper cites.
B. Dhingra, J. R. Cole, J. M. Eisenschlos, D. Gillick, J. Eisenstein, and W. W. Cohen, “Time-aware language models as temporal knowledge bases,” Trans. Assoc. Comput. Linguistics
2022
Earlier work this paper cites.

Similar

Y. Du, A. Bosselut, and C. D. Manning, “Synthetic disinformation attacks on automated fact verification systems,” in Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022
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Cited alongside, same era.
F. Yu, H. Zhang, P. Tiwari, and B. Wang, “Natural language reasoning, a survey,” 2023
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C. Wang, X. Liu, Y. Yue, X. Tang, T. Zhang, J. Cheng, Y. Yao, W. Gao, X. Hu, Z. Qi, Y. Wang, L. Yang, J. Wang, X. Xie, Z. Zhang, and Y. Zhang, “Survey on factuality in large language models: Knowledge, retrieval and domain-specificity,” CoRR
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Z. Xi, W. Chen, X. Guo, W. He, Y. Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhou, R. Zheng, X. Fan, X. Wang, L. Xiong, Y. Zhou, W. Wang, C. Jiang, Y. Zou, X. Liu, Z. Yin, S. Dou, R. Weng, W. Cheng, Q. Zhang, W. Qin, Y. Zheng, X. Qiu, X. Huan, and T. Gui, “The rise and potential of large language model based agents: A survey,” CoRR

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G. Chen, S. Dong, Y. Shu, G. Zhang, J. Sesay, B. F. Karlsson, J. Fu, and Y. Shi, “Autoagents: A framework for automatic agent generation,” CoRR
Original
2023
Later among the works it cites.
C. Qian, X. Cong, C. Yang, W. Chen, Y. Su, J. Xu, Z. Liu, and M. Sun, “Communicative agents for software development,” CoRR
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2023
Later among the works it cites.
S. Hong, X. Zheng, J. Chen, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou, C. Ran, L. Xiao, and C. Wu, “Metagpt: Meta programming for multi-agent collaborative framework,” CoRR
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2023
Later among the works it cites.
T. Shen, R. Jin, Y. Huang, C. Liu, W. Dong, Z. Guo, X. Wu, Y. Liu, and D. Xiong, “Large language model alignment: A survey,” CoRR
Original
2023
Later among the works it cites.

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Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities · Around

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O. Levy, M. Seo, E. Choi, and L. Zettlemoyer, “Zero-shot relation extraction via reading comprehension,” in Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada, August 3-4, 2017
2017
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al
2019
Earlier work this paper cites.
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. F. Christiano, and G. Irving, “Fine-tuning language models from human preferences,” CoRR
Original
2019
Earlier work this paper cites.
F. Petroni, T. Rocktäschel, S. Riedel, P. S. H. Lewis, A. Bakhtin, Y. Wu, and A. H. Miller, “Language models as knowledge bases?,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019
2019
Earlier work this paper cites.
N. Stiennon, L. Ouyang, J. Wu, D. M. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano, “Learning to summarize with human feedback,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Earlier work this paper cites.
A. Roberts, C. Raffel, and N. Shazeer, “How much knowledge can you pack into the parameters of a language model?,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021
2021
Earlier work this paper cites.
M. Geva, R. Schuster, J. Berant, and O. Levy, “Transformer feed-forward layers are key-value memories,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021
2021
Earlier work this paper cites.
N. D. Cao, W. Aziz, and I. Titov, “Editing factual knowledge in language models,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” in The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022
2022
Earlier work this paper cites.
K. Meng, D. Bau, A. Andonian, and Y. Belinkov, “Locating and editing factual associations in GPT,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022
2022
Earlier work this paper cites.
S. S. Raman, V. Cohen, E. Rosen, I. Idrees, D. Paulius, and S. Tellex, “Planning with large language models via corrective re-prompting,” CoRR
Original
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. F. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022
2022
Earlier work this paper cites.
D. Dai, L. Dong, Y. Hao, Z. Sui, B. Chang, and F. Wei, “Knowledge neurons in pretrained transformers,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022
2022
Earlier work this paper cites.
H. Chase, “LangChain,” Oct. 2022
2022
Earlier work this paper cites.
A. Madaan, N. Tandon, P. Clark, and Y. Yang, “Memory-assisted prompt editing to improve GPT-3 after deployment,” CoRR
Original
2022
Earlier work this paper cites.
K. Luu, D. Khashabi, S. Gururangan, K. Mandyam, and N. A. Smith, “Time waits for no one! analysis and challenges of temporal misalignment,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022
2022
Earlier work this paper cites.
B. Dhingra, J. R. Cole, J. M. Eisenschlos, D. Gillick, J. Eisenstein, and W. W. Cohen, “Time-aware language models as temporal knowledge bases,” Trans. Assoc. Comput. Linguistics
2022
Earlier work this paper cites.

Similar

Y. Du, A. Bosselut, and C. D. Manning, “Synthetic disinformation attacks on automated fact verification systems,” in Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022
2022
Cited alongside, same era.
F. Yu, H. Zhang, P. Tiwari, and B. Wang, “Natural language reasoning, a survey,” 2023
2023
Cited alongside, same era.
C. Wang, X. Liu, Y. Yue, X. Tang, T. Zhang, J. Cheng, Y. Yao, W. Gao, X. Hu, Z. Qi, Y. Wang, L. Yang, J. Wang, X. Xie, Z. Zhang, and Y. Zhang, “Survey on factuality in large language models: Knowledge, retrieval and domain-specificity,” CoRR
Original
2023
Cited alongside, same era.
Z. Xi, W. Chen, X. Guo, W. He, Y. Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhou, R. Zheng, X. Fan, X. Wang, L. Xiong, Y. Zhou, W. Wang, C. Jiang, Y. Zou, X. Liu, Z. Yin, S. Dou, R. Weng, W. Cheng, Q. Zhang, W. Qin, Y. Zheng, X. Qiu, X. Huan, and T. Gui, “The rise and potential of large language model based agents: A survey,” CoRR

Then

G. Chen, S. Dong, Y. Shu, G. Zhang, J. Sesay, B. F. Karlsson, J. Fu, and Y. Shi, “Autoagents: A framework for automatic agent generation,” CoRR
Original
2023
Later among the works it cites.
C. Qian, X. Cong, C. Yang, W. Chen, Y. Su, J. Xu, Z. Liu, and M. Sun, “Communicative agents for software development,” CoRR
Original
2023
Later among the works it cites.
S. Hong, X. Zheng, J. Chen, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou, C. Ran, L. Xiao, and C. Wu, “Metagpt: Meta programming for multi-agent collaborative framework,” CoRR
Original
2023
Later among the works it cites.
T. Shen, R. Jin, Y. Huang, C. Liu, W. Dong, Z. Guo, X. Wu, Y. Liu, and D. Xiong, “Large language model alignment: A survey,” CoRR
Original
2023
Later among the works it cites.

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Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities

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