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Register a new userContrastive Explanations for Model Interpretability
تفسيرات تناقض لتفسير النموذج
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توضيحات تناقض توضيح سبب حدوث حدث قد حدث على عكس آخر.إنهم بطبيعتهم بديهية للبشر لكل من الإنتاج والفهم.نقترح طريقة لإنتاج تفسيرات صغيرة في الفضاء الكامن، من خلال إسقاط تمثيل الإدخال، بحيث يتم التقاط الميزات التي تفرق إلا عن قرارات محتملة.يسمح التعديل لدينا بسلوك نموذجي للنظر في التفكير المتعرج فقط، والكشف عن جوانب المدخلات مفيدة لقرارات ومعاكضة معينة.يمكن أن تجيب تفسيراتنا الصنع للإجابة على أي تسمية، ومعها الملصق البدائل، هي ميزة إدخال معينة مفيدة.نحن ننتج تفسيرات صغيرة عبر كل من إسناد مفهوم مجردة رفيع المستوى ومستوى المدخلات / المدخلات منخفضة المستوى لإسناد معايير تصنيف NLP.توضح نتائجنا قدرة التفسيرات على نطاق واسع لتوفير إمكانية الترجمة الترجمة الفورية للقرارات النموذجية.
Contrastive explanations clarify why an event occurred in contrast to another. They are inherently intuitive to humans to both produce and comprehend. We propose a method to produce contrastive explanations in the latent space, via a projection of the input representation, such that only the features that differentiate two potential decisions are captured. Our modification allows model behavior to consider only contrastive reasoning, and uncover which aspects of the input are useful for and against particular decisions. Our contrastive explanations can additionally answer for which label, and against which alternative label, is a given input feature useful. We produce contrastive explanations via both high-level abstract concept attribution and low-level input token/span attribution for two NLP classification benchmarks. Our findings demonstrate the ability of label-contrastive explanations to provide fine-grained interpretability of model decisions.
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https://aclanthology.org/
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