Филолог заявил о массовой отмене обращения на «вы» с большой буквы09:36
巴德尔表示，在阿曼斡旋下，伊美谈判取得前所未有的进展，但美国、以色列竟然抛弃已有谈判成果，发动战争，令人遗憾。这场战争持续下去，将导致更多人员伤亡和财产损失。各方当前应共同努力推动尽早停火止战。中方作为安理会常任理事国，始终遵循联合国宪章宗旨原则，是可依赖的积极力量。在当前敏感时刻和复杂形势下，期待中方发挥重要作用。阿方将全力维护在阿中国公民和机构的安全。。业内人士推荐safew官方版本下载作为进阶阅读
，更多细节参见体育直播
Американскому сенатору стало «страшнее, чем когда либо» после брифинга по Ирану02:37
В КСИР выступили с жестким обращением к США и Израилю22:46。搜狗输入法下载是该领域的重要参考
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.