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Register a new userKnowledge Distillation with Noisy Labels for Natural Language Understanding
تقطير المعرفة مع ملصقات صاخبة لفهم اللغة الطبيعية
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يتم استخدام تقطير المعرفة (KD) على نطاق واسع لضغط ونشر نماذج لغة كبيرة مدربة مسبقا على أجهزة EDGE لتطبيقات العالم الحقيقي.ومع ذلك، فإن مساحة البحث واحدة مهملة هي تأثير الملصقات الصاخبة (التالفة) على KD.نقدم، إلى حد علمنا، أول دراسة حول الملكية الدماغية مع ملصقات صاخبة في فهم اللغة الطبيعية (NLU).نحن توثق نطاق المشكلة وتقديم طريقتين لتخفيف تأثير ضوضاء التسمية.تشير التجارب على مرجع الغراء إلى أن أساليبنا فعالة حتى تحت مستويات ضوضاء عالية.ومع ذلك، تشير نتائجنا إلى أن المزيد من البحث ضروري للتعامل مع ضجيج الملصقات تحت KD.
Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications. However, one neglected area of research is the impact of noisy (corrupted) labels on KD. We present, to the best of our knowledge, the first study on KD with noisy labels in Natural Language Understanding (NLU). We document the scope of the problem and present two methods to mitigate the impact of label noise. Experiments on the GLUE benchmark show that our methods are effective even under high noise levels. Nevertheless, our results indicate that more research is necessary to cope with label noise under the KD.
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