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Register a new userWhat Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
ما هي التغييرات التي يمكن أن تحضر نماذج اللغة واسعة النطاق؟دراسة مكثفة على Hyperclova: المحولات الإدارية الكورية بمليارات المليارات
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تعرض GPT-3 قدرة تعليمية ملحوظة في السياق من نماذج اللغة واسعة النطاق (LMS) المدربين على مئات البيانات بمليارات النطاق. نحن هنا تعالج بعض المشكلات المتبقية أقل إبلاغ عن ورق GPT-3، مثل LM غير الإنجليزية، وعروض النماذج المختلفة الحجم، وتأثير التحسين الفوري الذي قدم مؤخرا على التعلم في السياق. لتحقيق ذلك، نقدم Hyperclova، وهو متنقل كوري من 82B GPT-3 المدربين على كوربوس كوري مرئد من الرموز 560B. يعرض HyperClova المعزز من خلال رفيعنا الكوري الخاص بنا، ويعزز HyperClova مع تكوين التدريب لدينا أحدث أداء التعلم الصفرية في السياق وعدد قليل من الأداء في مهام المصب المختلفة في الكورية. أيضا، نعرض فوائد أداء التعلم الفوري وإظهار كيفية دمجه في خط أنابيب الهندسة السريعة. ثم نناقش إمكانية تحقيق نموذج لا يوجد رمز من خلال توفير قدرات النماذج الأولية ل AI لغير خبراء ML عن طريق إدخال ستوديو HyperClova، وهي واجهة هندسة سريعة التفاعلية. أخيرا، نوضح إمكانات أساليبنا بثلاث تطبيقات ناجحة في المنزل.
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning. To achieve this, we introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of prompt-based learning and demonstrate how it can be integrated into the prompt engineering pipeline. Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface. Lastly, we demonstrate the potential of our methods with three successful in-house applications.
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https://aclanthology.org/
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