发现语言模型会盲目附和用户，提出新方法提升不确定性的可信度。
Accounting for Sycophancy in Language Model Uncertainty Estimation
- 扩展了讨好偏见定义，量化其对模型不确定性判断的影响。
- 实验表明用户自信程度显著放大了模型的讨好倾向。
- 提出SyRoUP算法，能更准确预测并缓解这种偏差，适合人机协作场景。
有效的人机协作需要机器学习模型能够外化不确定性，以便用户在必要时进行反思和干预。对于语言模型而言，这种不确定性表征可能受到讨好偏见的影响：即倾向于同意用户意见，即使用户是错误的。例如，当用户给出错误解法时，模型可能表现出过高的自信。本文首次研究讨好偏见与不确定性估计之间的关系。我们提出一种讨好偏见的新泛化定义，用于衡量其对不确定性估计的下游影响，并提出一种新算法SyRoUP，以在不确定性估计过程中考虑讨好因素。不同于以往研究，本文考察了多种用户行为，包括用户建议的正确性和置信度，分析模型回答及其确定性如何变化。在对话预测和问答任务上的实验表明，用户置信度在调节讨好效应中起关键作用，且SyRoUP能更好地预测这些影响。由此我们主张，同时外化模型和用户的不确定性，有助于缓解讨好偏见的影响。
原文摘要 · Abstract (English)
Effective human-machine collaboration requires machine learning models to externalize uncertainty, so users can reflect and intervene when necessary. For language models, these representations of uncertainty may be impacted by sycophancy bias: proclivity to agree with users, even if they are wrong. For instance, models may be over-confident in (incorrect) problem solutions suggested by a user. We study the relationship between sycophancy and uncertainty estimation for the first time. We propose a generalization of the definition of sycophancy bias to measure downstream impacts on uncertainty estimation, and also propose a new algorithm (SyRoUP) to account for sycophancy in the uncertainty estimation process. Unlike previous works on sycophancy, we study a broad array of user behaviors, varying both correctness and confidence of user suggestions to see how model answers (and their certainty) change. Our experiments across conversation forecasting and question-answering tasks show that user confidence plays a critical role in modulating the effects of sycophancy, and that SyRoUP can better predict these effects. From these results, we argue that externalizing both model and user uncertainty can help to mitigate the impacts of sycophancy bias.
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