用行为数据生成个性沟通画像，让AI更真实地代表个人说话。
Mimetic Alignment with ASPECT: Evaluation of AI-inferred Personal Profiles
- 通过职场行为数据评估沟通特质，无需为每人微调模型。
- 生成的画像与自评中度一致，回复更受好评且差异明显。
- 可追溯证据帮助用户发现偏差、修正自我认知、协商合适表达。
能够代表个体沟通的AI代理需要捕捉每个人真实的表达方式，但现有方法要么需昂贵的个性化微调，要么产出通用化结果，或仅优化偏好而忽视沟通风格。我们提出ASPECT（自动化社会心理沟通特质评估），一种无需个性化训练即可利用大模型分析工作场所行为数据、对照经验证的沟通量表评估个体特质的流程。在20名参与者的研究中（1,840对项目评分，600个情景评估），ASPECT生成的画像与自评达到中度一致性；其生成回复在整体上优于通用基线和自报告基线，且个体与情景间差异显著。在画像审核阶段，关联证据帮助参与者识别误判、调整自我评价，并协商情境适配的表达方式。研究揭示了构建可审计、个体化沟通画像的路径，使个体能自主掌控工作场景中代理对其形象的呈现。
原文摘要 · Abstract (English)
AI agents that communicate on behalf of individuals need to capture how each person actually communicates, yet current approaches either require costly per-person fine-tuning, produce generic outputs from shallow persona descriptions, or optimize preferences without modeling communication style. We present ASPECT (Automated Social Psychometric Evaluation of Communication Traits), a pipeline that directs LLMs to assess constructs from a validated communication scale against behavioral evidence from workplace data, without per-person training. In a case study with 20 participants (1,840 paired item ratings, 600 scenario evaluations), ASPECT-generated profiles achieved moderate alignment with self-assessments, and ASPECT-generated responses were preferred over generic and self-report baselines on aggregate, with substantial variation across individuals and scenarios. During the profile review phase, linked evidence helped participants identify mischaracterizations, recalibrate their own self-ratings, and negotiate context-appropriate representations. We discuss implications for building inspectable, individually scoped communication profiles that let individuals control how agents represent them at work.
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