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Register a new userRelational World Knowledge Representation in Contextual Language Models: A Review
العلال العالمي تمثيل المعرفة في نماذج اللغة السياقية: مراجعة
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عادة ما تستخدم قواعد المعرفة العلوية (KBS) لتمثيل المعرفة العالمية في الآلات. ومع ذلك، في حين أن مفيدة لدرجة عالية من الدقة والتفسيرية، عادة ما يتم تنظيم KBS وفقا للخطط المعرفة يدويا، والتي تحد من تعبيرها وتتطلب جهود إنسانية كبيرة للمهندس والصيانة. في هذا الاستعراض، نأخذ منظور معالجة لغات طبيعية لهذه القيود، وفحص كيفية معالجةها جزئيا من خلال تدريب نماذج اللغة السياقية العميقة (LMS) لاستيعابها والتعبير عن المعرفة العلاجية بأشكال أكثر مرونة. نقترح تنظيم استراتيجيات تمثيل المعرفة في LMS بواسطة مستوى إشراف KB المقدمة، من أي إشراف KB على الإشراف على مستوى الكيان والعلاقات. مساهماتنا هي ثلاثة أضعاف: (1) نحن نقدم تصنيفا رفيع المستوى، توسع لتمثيل المعرفة في LMS؛ (2) ضمن تصنيفنا، نسلط الضوء على النماذج البارزة ومهام التقييم والنتائج، من أجل تقديم استعراض محدث لقدرات تمثيل المعرفة الحالية في LMS؛ و (3) نقترح اتجاهات البحث في المستقبل التي تبني على الجوانب التكميلية ل LMS و KBS كتمثيل المعرفة.
Relational knowledge bases (KBs) are commonly used to represent world knowledge in machines. However, while advantageous for their high degree of precision and interpretability, KBs are usually organized according to manually-defined schemas, which limit their expressiveness and require significant human efforts to engineer and maintain. In this review, we take a natural language processing perspective to these limitations, examining how they may be addressed in part by training deep contextual language models (LMs) to internalize and express relational knowledge in more flexible forms. We propose to organize knowledge representation strategies in LMs by the level of KB supervision provided, from no KB supervision at all to entity- and relation-level supervision. Our contributions are threefold: (1) We provide a high-level, extensible taxonomy for knowledge representation in LMs; (2) Within our taxonomy, we highlight notable models, evaluation tasks, and findings, in order to provide an up-to-date review of current knowledge representation capabilities in LMs; and (3) We suggest future research directions that build upon the complementary aspects of LMs and KBs as knowledge representations.
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The factual knowledge acquired during pre-training and stored in the parameters of Language Models (LMs) can be useful in downstream tasks (e.g., question answering or textual inference). However, some facts can be incorrectly induced or become obsol
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