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Register a new userTowards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers
نحو فهم شامل وتقييم دقيق للتحيزات المجتمعية في المحولات المدربة مسبقا
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وقد مكن سهولة الوصول إلى المحولات المدربين مسبقا المطورين إلى الاستفادة من نماذج اللغة واسعة النطاق لبناء تطبيقات مثيرة لمستخدميها.في حين توفر هذه النماذج المدربة مسبقا نقاط انطلاق مريحة للباحثين والمطورين، فهناك القليل من النظر في التحيزات المجتمعية التي تم التقاطها داخل هذه النموذج المخاطرة بإدانة التحيزات العنصرية والجنسية وغيرها من التحيزات الضارة عند نشر هذه النماذج على نطاق واسع.في هذه الورقة، نحقق في تنظيم النوع الاجتماعي والعنصري عبر النماذج اللغوية المدربة مسبقا في كل مكان، بما في ذلك GPT-2، XLNet، Bert، روبرتا، ألبرت والتقطير.نحن نقيم التحيز داخل المحولات المدربة مسبقا باستخدام ثلاثة مقاييس: Weat، احتمال التسلسل، وتصنيف الضمير.نستنتج مع تجربة توضح عدم فعالية تقنيات تضمين الكلمات، مثل Weat، مما يشير إلى الحاجة إلى اختبار التحيز الأكثر قوة في المحولات.
The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users. While such pre-trained models offer convenient starting points for researchers and developers, there is little consideration for the societal biases captured within these model risking perpetuation of racial, gender, and other harmful biases when these models are deployed at scale. In this paper, we investigate gender and racial bias across ubiquitous pre-trained language models, including GPT-2, XLNet, BERT, RoBERTa, ALBERT and DistilBERT. We evaluate bias within pre-trained transformers using three metrics: WEAT, sequence likelihood, and pronoun ranking. We conclude with an experiment demonstrating the ineffectiveness of word-embedding techniques, such as WEAT, signaling the need for more robust bias testing in transformers.
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