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Register a new userProbing Contextual Language Models for Common Ground with Visual Representations
التحقيق نماذج اللغة السياقية الأرضية المشتركة مع تمثيلات مرئية
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اجتذبت نجاح نماذج اللغة السياقية واسعة النطاق اهتماما كبيرا بتحقيق ما يتم ترميزه في تمثيلاتهم.في هذا العمل، نعتبر سؤالا جديدا: إلى أي مدى يتم محاذاة تمثيل السياق للأسماء الخرسانية مع التمثيلات المرئية المقابلة؟نقوم بتصميم نموذج التحقيق الذي يقيم مدى فعالية تميز النصوص النصية فقط في التمييز بين مطابقة العروض المرئية غير المطابقة.تظهر النتائج الخاصة بنا أن تمثيلات اللغة وحدها توفر إشارة قوية لاسترداد تصحيحات الصورة من فئات الكائنات الصحيحة.علاوة على ذلك، فهي فعالة في استرداد حالات محددة من بقع الصور؛يلعب السياق النصي دورا مهما في هذه العملية.نماذج اللغة الترطفة بصريا تتفوق قليلا على نماذج اللغة النصية فقط في حالة استرجاع مثيل، ولكن تحت أداء البشر بشكل كبير.نأمل أن تلهم تحليلاتنا بالبحث في المستقبل في فهم وتحسين القدرات البصرية لنماذج اللغة.
The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.
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