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Register a new userUncovering Implicit Gender Bias in Narratives through Commonsense Inference
يكشف التحيز بين الجنسين الضمني في الروايات من خلال استنتاج المنطقي
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تعلم نماذج اللغة المدربة مسبقا تحيزات ضارة اجتماعيا من كورسا التدريب الخاصة بهم، وقد تكرر هذه التحيزات عند استخدامها للجيل.ندرس التحيزات الجنسانية المرتبطة بطل الرواية في القصص الناتجة النموذجية.قد يتم التعبير عن هذه التحيزات إما صراحة (لا تستطيع المرأة أن تجمع ") أو ضمنيا (على سبيل المثال طابع الذكور غير المرغوب فيه يرشدها إلى مساحة وقوف السيارات).نحن نركز على التحيزات الضمنية واستخدام محرك منطق المنطقي للكشف عنها.على وجه التحديد، نستنتج وتحليل دوافع بطل الرواية، والسمات، والدول الذهنية، والآثار على الآخرين.تتماشى نتائجنا المتعلقة بالتحيزات الضمنية مع العمل المسبق الذي درس تحيزات صريحة، على سبيل المثال إظهار أن تصوير الأحرف الإناث يتركز حول المظهر، بينما تركز أرقام الذكور على الفكر.
Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly (women can't park'') or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist's motivations, attributes, mental states, and implications on others. Our findings regarding implicit biases are in line with prior work that studied explicit biases, for example showing that female characters' portrayal is centered around appearance, while male figures' focus on intellect.
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