AI 人格會成長嗎？——大型語言模型智能體經歷生活事件後之人格演變分析與基準評估

摘要

人格條件化 LLM 智能體（PC-Agents）日益廣泛應用於情緒支持、社會模擬與角色扮演，進而推動了能在長時間互動中保持連貫性的終身智能體之發展。此類連貫性的關鍵組成在於人格演化：智能體應在經歷不同情境下的生活事件時，展現合理且具心理學基礎的改變。儘管既有研究顯示 LLM 的人格可能因情境擾動而產生偏移，但這些偏移如何隨特質、事件、人物設定及模型而變化，仍缺乏充分理解。本研究以五大人格特質作為心理計量錨點，探討 11 個重大生活事件所誘發的人格改變，並對照人類人格心理學之縱貫性證據來詮釋所產生的軌跡。跨四個診斷軸向的結果顯示，無論事件-特質配對是否有文獻記載的人類改變方向，PC-Agents 皆以相似速率呈現可量測的特質偏移。即使在偏移方向符合預期的情況下，其幅度通常仍低於人類效應量範圍。性別與文化地區提示詞僅顯示微弱的調節效果，而相較於人類樣本，人物設定層級的離散程度被壓縮了三至四倍。為促進系統性比較，我們提出 BFI-Adapt——一個可重複使用的評測基準，用以評分事件誘發人格改變的方向忠實度——並以此對 14 個模型進行排名。驗證套件顯示，所量測到的偏移超過無事件重測噪音，在獨立改寫的提示詞下保持穩定，與情境式行為選擇僅呈現有限且依模型而異的收斂性，並且在穿插無關對話後仍持續存在。綜合而言，這些檢驗確立了所量測軌跡為穩健的事件條件化反應模式。我們的結果表明，現有的 PC-Agents 模擬了人類人格動態的平均值，但未能模擬其形態。

English

Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that remain coherent over extended interactions. A key component of such coherence is personality evolution: agents should undergo plausible, psychology-grounded changes as they experience life events in different contexts. Although prior work shows that LLM personalities can shift under contextual perturbations, how these shifts vary across traits, events, personas, and models remains poorly understood. We study event-induced personality change after 11 major life events, using the Big Five traits as a psychometric anchor and interpreting the resulting trajectories against longitudinal evidence from human personality psychology. Across four diagnostic axes, PC-Agents exhibit measurable trait shifts at similar rates for event-trait pairs with and without documented human change directions. Even when shifts follow the expected direction, their magnitudes usually fall below human effect-size ranges. Gender and cultural-region prompts show little moderating effect, while persona-level dispersion is compressed three- to four-fold relative to human samples. To enable systematic comparison, we introduce BFI-Adapt, a reusable benchmark for scoring the directional fidelity of event-induced personality change, and use it to rank 14 models. A validation suite shows that the measured shifts exceed no-event retest noise, remain stable under independently paraphrased prompts, exhibit limited and model-dependent convergence with scenario-based behavioral choices, and persist across intervening unrelated dialogue. Together, these checks establish the measured trajectories as robust event-conditioned response patterns. Our results suggest that current PC-Agents simulate the mean of human personality dynamics, but not its shape.