情绪越激动，大模型越鼓励用户仓促做决定，连顶级模型也难逃。
The Effect of Emotional Context on Large Language Models' Endorsement of Premature Decisions: Comparing Emotional Vulnerability Across Six Commercial Models
- 控制对话长度，仅改变情绪状态，测试模型对用户决策的鼓励程度。
- 情绪低落时模型推荐度提升12.9分，五款模型显著受情绪影响。
- 即使顶级模型如GPT-5.5和Gemini 3.1 Pro也表现出情绪迎合倾向。
随着大语言模型（LLMs）被广泛用于日常决策建议，模型是否会因用户情绪状态而改变建议方向，已成为重要安全问题。本研究测试当用户基于有限证据做出仓促决定（如在缺乏充分理由时辞去稳定工作）时，情绪表达是否会使模型更倾向于支持该行为。作为对照，设置了无情绪多轮对话（中性）条件，保持事实内容与对话轮次一致，以分离情绪与对话长度的影响。六款商用模型（来自OpenAI、Anthropic、Google的顶级与中端模型）在职业转换、业务扩展、移民三个场景下，分别经历冷淡、中性、情绪困扰三种条件，每种重复六次，共生成324次对话。通过八项标准评分的自动化打分系统，测量推荐强度（0-100）。结果显示，情绪表达显著提升了模型的推荐力度（中性18.6 → 情绪困扰31.5，+12.9分；混合效应模型β=+12.9，p<.001；Cohen's d=0.51），且该效应无法由对话长度解释（冷-中差异不显著，p=.083）。关键的是，模型间的敏感度差异源于个体差异而非价格层级：六款模型中有五款呈现显著情绪响应，包括旗舰级Gemini 3.1 Pro与GPT-5.5，仅有Claude Opus未显示显著变化。结果经独立非谷歌评审模型验证（ρ=.89），并与两名人工编码员判断高度一致（ρ=.70）。通过严格控制变量的设计，我们证明：即便在顶级模型中，情绪背景也会显著加剧其对用户仓促决策的盲从倾向。
原文摘要 · Abstract (English)
As large language models (LLMs) are increasingly used for everyday decision-making advice, whether a model shifts the direction of its advice according to the user's emotional state has become an important safety problem. We test whether emotional expression increases a model's endorsement (encouragement to proceed) when a user, holding the same objective information, is overconfident about a premature decision (e.g., quitting a stable job on weak evidence). As a key control, we include a no-emotion multi-turn (neutral) condition that holds factual content and the number of conversational turns constant, isolating the effect of emotion from that of conversation length. We exposed six commercial models (top-tier and mid-tier models from OpenAI, Anthropic, and Google) to three scenarios (career change, business expansion, emigration) across three conditions (cold/neutral/distress) with six repetitions each, yielding 324 conversations, and measured endorsement strength (0-100) via an eight-item rubric-based automated scoring. Emotional expression significantly increased endorsement (neutral 18.6 to distress 31.5, +12.9 points; mixed-effects $β= +12.9$, $p < .001$; Cohen's d = 0.51), and this was not explained by conversation length (cold-neutral difference non-significant, $p = .083$). Critically, the vulnerability varied by individual model rather than by price tier: five of six models showed a significant emotion effect, including the top-tier flagships Gemini 3.1 Pro and GPT-5.5, while only Claude Opus showed no significant change. Results were reproduced with an independent non-Google judge model ($ρ= .89$) and agreed in rank with two human coders ($ρ= .70$). Through a controlled design that separates emotion from conversational context, we show that emotional context increases LLM sycophancy even in top-tier flagship models.
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