في الإعدادات الاجتماعية، يخضع الكثير من السلوك البشري قواعد سلوك غير معلن في المعايير المجتمعية. بالنسبة للأنظمة الاصطناعية التي سيتم دمجها بالكامل في البيئات الاجتماعية، فإن الالتزام بهذه القواعد هو شرط أساسي. للتحقيق في ما إذا كانت نماذج توليد اللغة يمكن أن تكون بمثابة مشاكل سلوكية للأنظمة المنتشرة في الإعدادات الاجتماعية، فإننا نقيم قدرتها على توليد أوصاف عمل تحقق أهدافا محددة مسبقا في القيود المعيارية. علاوة على ذلك، نحن ندرس إذا كانت النماذج يمكن أن تتوقع عواقب من المحتمل إجراءات إما مراقبة أو تنتهك المعايير المعروفة، أو شرح سبب تفضيل بعض الإجراءات من خلال توليد فرضيات المعايير ذات الصلة. لهذا الغرض، نقدم قصصا أخلاقية، ومجموعة بيانات من جمهور الحشد من الروايات المنظمة، المتفرعة لدراسة المنطق الاجتماعي المحدد، الموجه نحو الأهداف. أخيرا، نقترح استراتيجيات فك التشفير التي تجمع بين نماذج خبراء متعددة لتحسين جودة الإجراءات الناتجة والآثار والمؤسسات القوية بشكل كبير.
In social settings, much of human behavior is governed by unspoken rules of conduct rooted in societal norms. For artificial systems to be fully integrated into social environments, adherence to such norms is a central prerequisite. To investigate whether language generation models can serve as behavioral priors for systems deployed in social settings, we evaluate their ability to generate action descriptions that achieve predefined goals under normative constraints. Moreover, we examine if models can anticipate likely consequences of actions that either observe or violate known norms, or explain why certain actions are preferable by generating relevant norm hypotheses. For this purpose, we introduce Moral Stories, a crowd-sourced dataset of structured, branching narratives for the study of grounded, goal-oriented social reasoning. Finally, we propose decoding strategies that combine multiple expert models to significantly improve the quality of generated actions, consequences, and norms compared to strong baselines.
References used
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