构建可追溯的实体情感画像基准，量化模型输出的情感倾向。
VIBE: A VAD-Informed Benchmark for Entity-Centered Affective Profiling of Large Language Model Outputs
- 分离生成与评分，明确区分整体与目标指向的情感维度。
- 验证情感三维度（价态、唤醒、支配）间不可相互替代，且评分具方向性。
- 提出情感护照报告格式，强调评分者身份与上下文信息的必要性。
大语言模型常描述具有社会意义的目标，如政治人物、国家、宗教等，不仅传递事实，还隐含情感基调：目标可能被呈现为正面或威胁性、平静或冲突、强大或脆弱。现有工作虽涵盖情感、倾向性、情绪等维度，但缺乏针对目标导向的VAD（价态-唤醒-支配）归因、明确评分协议与护照式报告格式。本文提出VIBE，一个面向实体中心情感画像的基准，其核心是测量协议：将生成与外部评分分离，区分标量倾向性、响应级VAD和目标导向的VAD，并通过情感护照报告画像。三个实证层支撑该协议：H1表明标量倾向性不能涵盖唤醒与支配，价态经人类标注交叉验证相关性高（rV=0.944，rV=0.954），而唤醒与支配为单评者方向性估计，与人工标注难度一致（rA=0.495，rD=0.702）；H2显示整体响应与目标导向的VAD存在本质差异，同一文本可整体情感一致但目标情感不同；H3为协议漂移诊断，揭示提示条件会改变情感画像，故每份报告需包含上下文元数据。研究支持将实体中心情感画像作为可记录实践：画像应附带评分者身份、覆盖范围、协议及解释边界。
原文摘要 · Abstract (English)
Large language models routinely describe socially salient targets, including political figures, countries, religions, organizations, historical events, and social groups, encoding affective framing alongside factual content: a target may appear favorable or threatening, calm or conflictual, powerful or vulnerable. Existing work captures parts of this space through sentiment, favorability, and emotion benchmarks, but none combines target-directed VAD attribution, an explicit scorer contract, and a passport reporting format. We introduce VIBE, a benchmark for entity-centered affective profiling of LLM outputs in Valence-Arousal-Dominance (VAD) space. Its core contribution is a measurement contract: VIBE separates generation from external scoring, distinguishes scalar favorability, response-level VAD, and target-directed VAD, and reports profiles through an Affective Passport. Three empirical layers support the contract. H1 shows scalar favorability does not subsume arousal and dominance: valence findings are cross-validated (rV = 0.944 judge-human, rV = 0.954 inter-scorer); arousal and dominance are single-scorer directional estimates, not point-precise, consistent with known inter-annotator difficulty on these axes (rA = 0.495, rD = 0.702 among human annotators). H2 shows whole-response and target-directed VAD are different contracts: the same text can carry one affective tone overall while representing the named target differently. H3 is a protocol-drift diagnostic: elicitation conditions shift profiles, motivating context metadata in every affective report. These results motivate entity-centered affective profiling as a documented practice: profiles should be released with scorer identity, coverage, protocol, and interpretation limits.
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