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التفكير النقدي لنماذج اللغة
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تأخذ هذه الورقة خطوة أولى نحو مناهج تفكير حرجة لنماذج اللغة التراجعية العصبية. نقدم لجنة اصطناعية من الحجج الصالحة تخصيصها، وتوليد نصوص جدلية اصطناعية لتدريب CRPIPT: محول تفكير حرج مدرب مسبقا مسبقا على أساس GPT-2. يمكن ملاحظة تأثيرات تعليمية نقل كبيرة: مدربة على ثلاث مخططات أساسية بسيطة، يكمل CRIPT بدقة استنتاجات مختلفة من أنواع الحجج المختلفة والمزيد. تعميم CRIPT مع مخططات الوسيطة الأساسية بطريقة صحيحة. علاوة على ذلك، نحصل على نتائج متسقة واعدة لمعايير NLU. على وجه الخصوص، تتجاوز دقة Cript الصفرية في تشخيص الغراء أداء GPT-2 بنسبة 15 نقطة مئوية. تشير النتائج إلى أن التدريب المسترد الوسيط على النصوص التي تجسد قدرات التفكير الأساسي (مثل مغطاة عادة في كتب التفكير الناقد) قد تساعد نماذج اللغة للحصول على مجموعة واسعة من مهارات المنطق. النصوص المنحجية الاصطناعية المقدمة في هذه الورقة هي نقطة انطلاق واعدة لبناء مناهج التفكير النقدي لنماذج اللغة. "
This paper takes a first step towards a critical thinking curriculum for neural auto-regressive language models. We introduce a synthetic corpus of deductively valid arguments, and generate artificial argumentative texts to train CRiPT: a critical thinking intermediarily pre-trained transformer based on GPT-2. Significant transfer learning effects can be observed: Trained on three simple core schemes, CRiPT accurately completes conclusions of different, and more complex types of arguments, too. CRiPT generalizes the core argument schemes in a correct way. Moreover, we obtain consistent and promising results for NLU benchmarks. In particular, CRiPT's zero-shot accuracy on the GLUE diagnostics exceeds GPT-2's performance by 15 percentage points. The findings suggest that intermediary pre-training on texts that exemplify basic reasoning abilities (such as typically covered in critical thinking textbooks) might help language models to acquire a broad range of reasoning skills. The synthetic argumentative texts presented in this paper are a promising starting point for building such a critical thinking curriculum for language models.''
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