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Register a new userDebiasing Methods in Natural Language Understanding Make Bias More Accessible
طرق debiasing في فهم اللغة الطبيعية تجعل التحيز أكثر قابلية للوصول
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غالبا ما يتم تحديد موكب النموذج إلى التحيز من خلال التعميم على مجموعات البيانات الخارجية المصممة بعناية.أساليب الدخل الحديثة في فهم اللغة الطبيعية (NLU) تحسين الأداء على مجموعات البيانات هذه عن طريق الضغط على النماذج في تحقيق تنبؤات غير متحيزة.الافتراض الأساسي وراء هذه الأساليب هو أن هذا يؤدي أيضا إلى اكتشاف ميزات أكثر قوة في التمثيلات الداخلية للنموذج.نقترح إطارا عاما يستند إلى التحقيق العامة يسمح بتفسير ما بعد الهوك للتحيزات في طرازات اللغة، واستخدام نهج نظرية معلومات لقياس قابلية استخراج بعض التحيزات من تمثيلات النموذج.نقوم بتجربة العديد من مجموعات بيانات NLU والتحيزات المعروفة، وتظهر ذلك، مضادا بشكل حدسي، كلما دفع نموذج لغة أكثر نحو نظام ديبي، فإن التحيز الأكثر ترميزا بالفعل في تمثيلاته الداخلية.
Model robustness to bias is often determined by the generalization on carefully designed out-of-distribution datasets. Recent debiasing methods in natural language understanding (NLU) improve performance on such datasets by pressuring models into making unbiased predictions. An underlying assumption behind such methods is that this also leads to the discovery of more robust features in the model's inner representations. We propose a general probing-based framework that allows for post-hoc interpretation of biases in language models, and use an information-theoretic approach to measure the extractability of certain biases from the model's representations. We experiment with several NLU datasets and known biases, and show that, counter-intuitively, the more a language model is pushed towards a debiased regime, the more bias is actually encoded in its inner representations.
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