超越固定心理人格：状态胜过特质，但语言模型是状态盲的
Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind
AI总结：
本文通过Chameleon数据集发现语言模型对状态盲，而奖励模型对用户状态反应不一致，推动情感计算和个性化对话研究。
摘要：
用户与语言模型的交互因用户静态属性（特质）和交互具体情境（状态）而异。然而，现有个性数据集（如PersonaChat、PANDORA等）仅捕捉特质，忽略状态影响。我们引入Chameleon数据集，包含5,001个情境心理档案，来自1,667名Reddit用户，每个用户在多个情境中测量。利用Chameleon数据集，我们得出三个关键发现：首先，受潜在状态-特质理论启发，分解方差发现74%在个体内部（状态）而仅26%在个体之间（特质）。其次，发现LLM是状态盲的：它们只关注特质，无论状态如何生成相似响应。第三，发现奖励模型对用户状态反应不一致：不同模型对同一用户倾向或惩罚方向相反。我们发布Chameleon以支持情感计算、个性化对话和RLHF对齐研究。
英文摘要：
User interactions with language models vary due to static properties of the user (trait) and the specific context of the interaction (state). However, existing persona datasets (like PersonaChat, PANDORA etc.) capture only trait, and ignore the impact of state. We introduce Chameleon, a dataset of 5,001 contextual psychological profiles from 1,667 Reddit users, each measured across multiple contexts. Using the Chameleon dataset, we present three key findings. First, inspired by Latent State-Trait theory, we decompose variance and find that 74% is within-person(state) while only 26% is between-person (trait). Second, we find that LLMs are state-blind: they focus on trait only, and produce similar responses regardless of state. Third, we find that reward models react to user state, but inconsistently: different models favor or penalize the same users in opposite directions. We release Chameleon to support research on affective computing, personalized dialogue, and RLHF alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
