Abstract:Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.
过去两年，大模型训练与推理带动数据中心用电量呈几何级增长。高盛研报显示，2026至2030年，数据中心将贡献美国电力需求增长的近40%，成为全美第一大新增用电负荷。加州、德州、弗吉尼亚等数据中心密集区，电价一年上涨超20%，居民电费压力直线飙升。。快连下载安装是该领域的重要参考
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内容与服务体系的建设，也将是未来的发展重点之一。买到玩具之后，如何让角色持续成长，如何维护用户与AI角色之间的关系，如何建立长期使用机制，这些问题都还有待一个更加成熟的解决方案。如果只是一个能对话的玩具，很难长期留住用户。，更多细节参见Safew下载
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