The stamp has a thin dark inner border line just inside the perforations, framing all content. Below this inner border line, there is a flat white horizontal strip spanning the full bottom width of the stamp, sitting inside the perforated edge. In the bottom-left of this white strip: the movie title in large heavy bold grotesque sans-serif font (similar to Franklin Gothic), in solid black. In the bottom-right of this white strip: the most accurate and natural Japanese kanji translation of the title or central theme of the movie in large bold black text, with small text above it reading “NIPPON 郵便”, and two lines of tiny black text below it — the first line showing the most iconic or recognizable location from the movie in all caps, and the second line showing the country where the movie was produced followed by a · and the year the movie was released — all right-aligned.
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.
，这一点在体育直播中也有详细论述
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Валентин Карант (редактор отдела БСССР)