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تعلم نماذج اللغة العصبية غير المعلمة (NLMS) التوزيعات التنبؤية للنص باستخدام مصدر بيانات خارجي، والذي يسمح لهم بالتعلم من خلال حفظ مخطط Datapooints التدريبي بشكل صريح.في حين أن هذه النماذج فعالة، فإن هذه النماذج غالبا ما تتطلب استرجاع من مؤشرات بيانات كبيرة في وقت الاختبار، مما يزيد بشكل كبير من تسليم الاستدلال، وبالتالي يحد من نشر NLMS غير المعلم في التطبيقات العملية.في هذه الورقة، نأخذ نموذج لغة Geature K-Neave المقترح مؤخرا كمثال، استكشاف الطرق لتحسين كفاءتها على طول الأبعاد المختلفة.تبين التجارب في معيار Wikitext-103 القياسي ومجموعات بيانات التكيف عن المجال أن أساليبنا قادرة على تحقيق ما يصل إلى سرعة 6X في سرعة الاستدلال مع الاحتفاظ بأداء مماثل.قد يوفر التحليل التجريبي الذي نقدمه مبادئ توجيهية للبحث في المستقبل يسعى إلى تطوير أو نشر أكثر كفاءة غير رسمية غير رسمية.
Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints. While effective, these models often require retrieval from a large datastore at test time, significantly increasing the inference overhead and thus limiting the deployment of non-parametric NLMs in practical applications. In this paper, we take the recently proposed k-nearest neighbors language model as an example, exploring methods to improve its efficiency along various dimensions. Experiments on the standard WikiText-103 benchmark and domain-adaptation datasets show that our methods are able to achieve up to a 6x speed-up in inference speed while retaining comparable performance. The empirical analysis we present may provide guidelines for future research seeking to develop or deploy more efficient non-parametric NLMs.
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