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本报北京3月2日电 （记者谷业凯）我国生成式人工智能用户规模和普及率快速提升。中国互联网络信息中心发布的第五十七次《中国互联网络发展状况统计报告》显示：截至2025年12月，我国生成式人工智能用户达6.02亿人，较2024年底增长141.7%；普及率达42.8%，同比大幅提高25.2个百分点。“十四五”时期，生成式人工智能加速融入生产生活，成为推动我国经济社会数字化、智能化转型的重要引擎。，详情可参考heLLoword翻译官方下载
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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.
Hurdle Word 3 answerCLUMP