建模复杂行为：视觉语言模型中的多人格组合与动态切换
Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models
AI总结：
本研究在视觉语言模型中引入显式人格条件，建立包括单人格、多人格和人格切换的系统评估框架，发现人格提示可提升图像描述但损害精确推理任务，并观察到多特质组合与动态切换中的平衡与残留效应。
摘要：
随着多模态大语言模型（MLLMs）在社交互动中的广泛部署，理解和控制其在复杂人格条件下的行为至关重要。本文引入显式人格条件，并建立了一个系统的评估框架，涵盖单人格诱导、多人格诱导和人格切换。实验表明，人格诱导能提升图像描述性能，但会损害需要精确推理的任务（如视觉问答）的性能。在多特质组合和动态切换过程中观察到平衡和残留效应，表明模型行为受到先前和当前人格约束的共同调节。现有的基于提示的人格诱导方法在多模态设置中表现出有限的迁移性。我们的工作揭示了MLLMs中人格建模的动态和复杂性质，并强调了针对人格诱导和评估的鲁棒、定制化方法的必要性。代码将在论文被接收后发布。
英文摘要：
With the widespread deployment of Multimodal Large Language Models (MLLMs) in social interaction, understanding and controlling their behavior under complex personality conditions is essential. This paper introduces explicit personality conditioning and establishes a systematic evaluation framework encompassing single-personality induction, multi-personality induction, and personality switching. Experiments show that personality induction improves image captioning performance but can impair performance on tasks requiring precise reasoning, such as visual question answering (VQA). Balancing and residual effects are observed during multi-trait composition and dynamic switching, indicating that model behavior is co-modulated by both previous and current personality constraints. Existing prompt-based personality induction methods show limited transferability to multimodal settings. Our work reveals the dynamic and complex nature of personality modeling in MLLMs and underscores the need for robust, tailored methods for personality induction and evaluation. The code will be released when the paper is accepted.
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。
