提出新方法检测并减轻大模型盲目迎合用户立场的问题
SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
- 用反事实提示法衡量模型在正负语言压力下的态度变化
- 发现模型越自信，越容易迎合用户，且现有方法效果有限
- 新策略让模型在不同假设下推理，有效抑制迎合但不牺牲响应性
大语言模型存在迎合倾向：无论对错，都会向用户表达的立场靠拢。现有研究虽关注此问题，但仍缺乏严谨的计算语言学度量。本文提出无监督的SWAY方法，通过反事实提示机制，对比模型在正面与负面语言压力下的回应变化，分离出框架效应。在6个基准模型上测试发现，模型的迎合行为随认知承诺程度升高而加剧。基于该度量，我们设计一种反事实思维链缓解策略，引导模型思考若相反假设成立时的答案。相比仅要求‘反迎合’的基线方法（效果中等且可能适得其反），我们的方法在多种模型、承诺水平和从句类型下将迎合降至接近零，同时保持对真实证据的敏感性。本工作贡献了可评测的迎合度量及由此指导的缓解方案。
原文摘要 · Abstract (English)
Large language models exhibit sycophancy: the tendency to shift outputs toward user-expressed stances, regardless of correctness or consistency. While prior work has studied this issue and its impacts, rigorous computational linguistic metrics are needed to identify when models are being sycophantic. Here, we introduce SWAY, an unsupervised computational linguistic measure of sycophancy. We develop a counterfactual prompting mechanism to identify how much a model's agreement shifts under positive versus negative linguistic pressure, isolating framing effects from content. Applying this metric to benchmark 6 models, we find that sycophancy increases with epistemic commitment. Leveraging our metric, we introduce a counterfactual mitigation strategy teaching models to consider what the answer would be if opposite assumptions were suggested. While baseline mitigation instructing to be explicitly anti-sycophantic yields moderate reductions, and can backfire, our counterfactual CoT mitigation drives sycophancy to near zero across models, commitment levels, and clause types, while not suppressing responsiveness to genuine evidence. Overall, we contribute a metric for benchmarking sycophancy and a mitigation informed by it.
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