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Register a new userThe SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
التحدي SelectGen: العثور على أفضل عينات تدريبية لتوليد النص العصبي القليل
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نقترح مهمة مشتركة على اختيار مثيل التدريب لعدد قليل من الجيل العصبي العصبي.أدت نماذج اللغة المحددة مسبقا على نطاق واسع إلى تحسينات مثيرة في جيل نص قليل.ومع ذلك، فإن كل العمل السابق تقريبا يطبق ما عليك سوى أخذ عينات عشوائية لتحديد مثيلات التدريب القليلة.لقد تم إيلاء الاهتمام ليس إلى استراتيجيات الاختيار وكيف سيؤثرون على أداء النموذج.دراسة استراتيجية الاختيار يمكن أن تساعدنا (1) الاستفادة القصوى من ميزانية التوضيحية الخاصة بنا في مهام المصب و (2) من أفضل النماذج الإندارية النصية ذات القليل من القصاصات.نرحب بالتقديمات التي تقدم استراتيجيات اختيارها والآثار على جودة الجيل.
We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. Studying the selection strategy can help us (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.
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