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Register a new userMultilingual Language Models Predict Human Reading Behavior
نماذج لغة متعددة اللغات تتوقع سلوك القراءة البشرية
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نقوم بتحليل ما إذا كانت نماذج اللغة الكبيرة قادرة على التنبؤ بأنماط سلوك القراءة البشرية.قارنا أداء نماذج محولات محول خاصة باللغات ومتعددة اللغات للتنبؤ بتدابير وقت القراءة التي تعكس معالجة الجملة البشرية الطبيعية على النصوص الهولندية والإنجليزية والألمانية والروسية.ينتج عن هذا نماذج دقيقة من سلوك القراءة البشرية، والذي يشير إلى أن نماذج المحولات ترميز ضمنيا أهمية نسبية في اللغة بطريقة مماثلة لآليات المعالجة البشرية.نجد أن نماذج بيرت و XLM تتنبأ بنجاح مجموعة من ميزات تتبع العين.في سلسلة من التجارب، نحلل القدرات عبر المجال واللغات الشاملة لهذه النماذج وإظهار كيف تعكس معالجة الجملة البشرية.
We analyze if large language models are able to predict patterns of human reading behavior. We compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing on Dutch, English, German, and Russian texts. This results in accurate models of human reading behavior, which indicates that transformer models implicitly encode relative importance in language in a way that is comparable to human processing mechanisms. We find that BERT and XLM models successfully predict a range of eye tracking features. In a series of experiments, we analyze the cross-domain and cross-language abilities of these models and show how they reflect human sentence processing.
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