Sycophancy in Large Language Models: Causes and Mitigations

Sugimoto, K.: entity-related-papers (2020), https://github.com/kaisugi/entity-related-papers

2020

Earlier work this paper cites.

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Original

2023

Earlier work this paper cites.

Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S.R., Cheng, N., Durmus, E., Hatfield-Dodds, Z., Johnston, S.R., Kravec, S., Maxwell, T., McCandlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., Perez, E.: Towards understanding sycophancy in language models. arXiv preprint arXiv:2310.13548 (2023), accessed on 27 Oct 2023

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2023

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Turpin, M., Michael, J., Perez, E., Bowman, S.R.: Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. In: Advances in Neural Information Processing Systems (2023), https://doi.org/10.48550/arxiv.2305.04388

2023

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Wei, J., Huang, D., Lu, Y., Zhou, D., Le, Q.: Simple synthetic data reduces sycophancy in large language models. arXiv preprint arXiv:2308.03958 (2023)

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2023

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Weston, J., Sukhbaatar, S.: System 2 attention (is something you might need too). arXiv preprint arXiv:2311.11829 (2023)

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2023

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Xie, Q., Wang, Z., Feng, Y., Xia, R.: Ask again, then fail: Large language models’ vacillations in judgment. arXiv preprint arXiv:2310.02174 (2023)

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Chen, C., Liu, Z., Jiang, W., Goh, S.Q., Lam, K.Y.: Trustworthy, responsible, and safe ai: A comprehensive architectural framework for ai safety with challenges and mitigations. arXiv preprint arXiv:2408.12935 (2024)

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2024

Cited alongside, same era.

Deng, C., Duan, Y., Jin, X., Chang, H., Tian, Y., Liu, H., Zou, H., Jin, Y., Xiao, Y., Wang, Y., Wu, S., Xie, Z., Gao, K., He, S., Zhuang, J., Cheng, L., Wang, H.: Deconstructing the ethics of large language models from long-standing issues to new-emerging dilemmas. arXiv preprint arXiv:2406.05392 (2024)

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2024

Cited alongside, same era.

Deng, Z., Guo, Y., Han, C., Ma, W., Xiong, J., Wen, S., Xiang, Y.: Ai agents under threat: A survey of key security challenges and future pathways. arXiv preprint arXiv:2406.02630 (2024)

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2024

Cited alongside, same era.

Denison, C., MacDiarmid, M., Barez, F., Duvenaud, D., Kravec, S., Marks, S., Schiefer, N., Soklaski, R., Tamkin, A., Kaplan, J., Shlegeris, B., Bowman, S., Perez, E., Hubinger, E.: Sycophancy to subterfuge: Investigating reward-tampering in large language models. arXiv preprint arXiv:2406.10162 (2024)

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Lu, T., Shen, L., Yang, X., Tan, W., Chen, B., Yao, H.: It takes two: On the seamlessness between reward and policy model in rlhf. arXiv preprint arXiv:2406.07971 (2024)

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2024

Closest in time.

RRV, A., Tyagi, N., Uddin, N., Varshney, N., Baral, C.: Chaos with keywords: Exposing large language models sycophantic hallucination to misleading keywords and evaluating defense strategies. arXiv preprint arXiv:2406.03827 (2024)

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2024

Closest in time.

Stickland, A., Lyzhov, A., Pfau, J., Mahdi, S., Bowman, S.: Steering without side effects: Improving post-deployment control of language models. arXiv preprint arXiv:2406.15518 (2024)

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2024

Closest in time.

Wen, J., Zhong, R., Khan, A., Perez, E., Steinhardt, J., Huang, M., Bowman, S., He, H., Feng, S.: Language models learn to mislead humans via rlhf. arXiv preprint arXiv:2409.12822 (2024)

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2024

Closest in time.