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Register a new userCrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
Crossfit: تحدي تعلم قليل بالرصاص للتعميم عبر المهام في NLP
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يمكن للبشر تعلم مهمة لغة جديدة بكفاءة مع عدد قليل فقط من الأمثلة، من خلال الاستفادة من معارفهم التي تم الحصول عليها عند تعلم المهام السابقة. في هذه الورقة، نستكشف ما إذا كان يمكن الحصول على قدرة تعميم المهام عبر المهام المباشرة، وتطبيقها بشكل أكبر على بناء عدد قليل من المتعلمين لقلة طلقات عبر مهام NLP المتنوعة. نقدم Crossfit، وهي إعداد مشكلة في دراسة قدرة تعميم المهام عبر المهام، والتي توحد من أقسام المهام / غير المرئية، والوصول إلى البيانات أثناء مراحل التعلم المختلفة، وبروتوكولات التقييم. لإنشاء أقسام مهمة مختلفة / غير مرئية في CrossFit وتسهيل التحليل المتعمق، نقدم صالة الألعاب الرياضية ذات القليل من الرصاص NLP، مستودع 160 مهام NLP قليلة متنوعة تم إنشاؤها من مجموعات بيانات NLP مفتوحة الوصول وتحويلها إلى نص موحد لتنسيق النص. يكشف تحليلنا أن قدرة التعلم القليلة من التعلم على المهام غير المرئية يمكن تحسينها عبر مرحلة التعلم المنبع باستخدام مجموعة من المهام المشاهدة. نلاحظ أيضا أن اختيار مهام التعلم المنبع يمكن أن يؤثر بشكل كبير على أداء قليل الطلقات على المهام غير المرئية، ويطلب المزيد من التحليلات حول التشابه المهمة وقابلية النقل.
Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how such cross-task generalization ability can be acquired, and further applied to build better few-shot learners across diverse NLP tasks. We introduce CrossFit, a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols. To instantiate different seen/unseen task partitions in CrossFit and facilitate in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse few-shot NLP tasks created from open-access NLP datasets and converted to a unified text-to-text format. Our analysis reveals that the few-shot learning ability on unseen tasks can be improved via an upstream learning stage using a set of seen tasks. We also observe that the selection of upstream learning tasks can significantly influence few-shot performance on unseen tasks, asking further analysis on task similarity and transferability.
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