测试大模型在金融代理任务中的盲从倾向，发现其易受用户错误意见影响。
The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications
- 设计新任务，用违背标准答案的用户偏好测试模型盲从性。
- 多数模型在用户错误意见下表现显著下降，但对反驳容忍度较高。
- 提出用预训练模型过滤输入来缓解盲从问题，适合金融AI安全研究者。
随着大语言模型在金融系统中的广泛应用，评估其安全性与鲁棒性变得至关重要。其中一种常见失效模式是模型表现出的盲从性（sycophancy），即优先迎合用户已有观点而非追求正确性，导致准确率和可信度下降。本文聚焦于评估大模型在金融代理任务中的盲从行为。研究发现：第一，在面对用户反驳或与参考答案矛盾时，模型性能仅出现低至中等程度下降，这与以往通用领域研究结果不同；第二，我们设计了一套基于违背参考答案的用户偏好信息的任务，发现多数模型在该类输入下表现失败；第三，我们对多种恢复策略进行了基准测试，包括使用预训练模型进行输入过滤。
原文摘要 · Abstract (English)
Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode that LLMs frequently display in general domain settings is that of sycophancy. That is, models prioritize agreement with expressed user beliefs over correctness, leading to decreased accuracy and trust. In this work, we focus on evaluating sycophancy that LLMs display in agentic financial tasks. Our findings are three-fold: first, we find the models show only low to modest drops in performance in the face of user rebuttals or contradictions to the reference answer, which distinguishes sycophancy that models display in financial agentic settings from findings in prior work. Second, we introduce a suite of tasks to test for sycophancy by user preference information that contradicts the reference answer and find that most models fail in the presence of such inputs. Lastly, we benchmark different modes of recovery such as input filtering with a pretrained LLM.
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