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Register a new userSustainable Modular Debiasing of Language Models
ديوان وحدات مستدامة للنماذج اللغوية
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التحيزات النمطية غير العادلة (على سبيل المثال، التحيزات الجنسانية أو العنصرية أو الدينية) ترميز نماذج اللغة الحديثة المحددة مسبقا (PLMS) لها آثار أخلاقية سلبية على الاعتماد الواسع النطاق لتكنولوجيا اللغات الحديثة. لعلاج ذلك، تم تقديم مجموعة واسعة من تقنيات المساواة مؤخرا لإزالة هذه التحيزات النمطية من PLMS. ومع ذلك، فإن طرق الدخل الحالية، ومع ذلك، قم بتعديل جميع معلمات PLMS مباشرة، والتي - إلى جانب كونها باهظة الثمن - مع خطر الكامنة من (كارثي) نسيان المعرفة اللغوية المفيدة المكتسبة في الاحتجاج. في هذا العمل، نقترح نهجا أكثر استدامة للدوائر على أساس محولات Deviasing المخصصة، التي دبلها أديل. بشكل ملموس، نحن (1) وحدات محول حقن في طبقات PLM الأصلية و (2) تحديث المحولات فقط (أي ونحن نعرض أديل، في الدخل الجنساني من BERT: تقييمنا الواسع، يشمل ثلاثة تدابير محلية خارجية ومثيرة للخدمة الخارجية، مما يجعل أديل، فعالة للغاية في تخفيف التحيز. نوضح كذلك - نظرا لطبيعتها المعيارية - أديل، إلى جانب محولات المهام، تحتفظ بالإنصاف حتى بعد التدريب على النمو النطاق واسع النطاق. وأخيرا، عن طريق بيرت متعددة اللغات، نجحنا في نقل أديل بنجاح إلى ست لغات مستهدفة.
Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology. To remedy for this, a wide range of debiasing techniques have recently been introduced to remove such stereotypical biases from PLMs. Existing debiasing methods, however, directly modify all of the PLMs parameters, which -- besides being computationally expensive -- comes with the inherent risk of (catastrophic) forgetting of useful language knowledge acquired in pretraining. In this work, we propose a more sustainable modular debiasing approach based on dedicated debiasing adapters, dubbed ADELE. Concretely, we (1) inject adapter modules into the original PLM layers and (2) update only the adapters (i.e., we keep the original PLM parameters frozen) via language modeling training on a counterfactually augmented corpus. We showcase ADELE, in gender debiasing of BERT: our extensive evaluation, encompassing three intrinsic and two extrinsic bias measures, renders ADELE, very effective in bias mitigation. We further show that -- due to its modular nature -- ADELE, coupled with task adapters, retains fairness even after large-scale downstream training. Finally, by means of multilingual BERT, we successfully transfer ADELE, to six target languages.
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