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Register a new userToo Much in Common: Shifting of Embeddings in Transformer Language Models and its Implications
الكثير من القمم لدينا: تحويل المدينات في نماذج لغة المحولات وآثارها
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يبدو أن نجاح النماذج اللغوية المستندة إلى بنية المحولات لا يتعارض مع خصائص الخواص المتجاهية الملحوظة التي تعلمتها هذه النماذج.نقوم بحل هذا من خلال إظهار، خلافا للدراسات السابقة، أن التمثيل لا تشغل مخروطا ضيقا، ولكن الانجراف في اتجاهات مشتركة إلى حد ما.عند أي خطوة تدريبية، يتم تحديث جميع المدينات باستثناء تضمين الهدف الأساسي للحقيقة مع التدرج في نفس الاتجاه.يضاعف فوق مجموعة التدريب، وانجرف المدينات وتبادل المكونات الشائعة، حيث تجلى في شكلها في جميع النماذج التي اختبرناها تجريبيا.تظهر تجاربنا أن iSotropy يمكن استعادتها باستخدام تحول بسيط.
The success of language models based on the Transformer architecture appears to be inconsistent with observed anisotropic properties of representations learned by such models. We resolve this by showing, contrary to previous studies, that the representations do not occupy a narrow cone, but rather drift in common directions. At any training step, all of the embeddings except for the ground-truth target embedding are updated with gradient in the same direction. Compounded over the training set, the embeddings drift and share common components, manifested in their shape in all the models we have empirically tested. Our experiments show that isotropy can be restored using a simple transformation.
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