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Register a new userAttribute Alignment: Controlling Text Generation from Pre-trained Language Models
محاذاة السمة: التحكم في جيل النص من نماذج اللغة المدربة مسبقا
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تستفيد نماذج اللغة الكبيرة من التدريب بكمية كبيرة من النص غير المسبق، مما يمنحهم قدرات توليد بطلاقة ومتنوعة بشكل متزايد.ومع ذلك، فإن استخدام هذه النماذج لتوليد النص الذي يأخذ في الاعتبار السمات المستهدفة، مثل قطبية المعالم أو مواضيع محددة، لا يزال يمثل تحديا.نقترح طريقة بسيطة ومرنة للسيطرة على جيل النص عن طريق محاذاة تمثيلات سمة Deventangled.على النقيض من الجهود الأخيرة التي يبذلها الجهود المبينة في تدريب تمييزي على توزيع مستوى الرمز المميز لسمة، نستخدم نفس البيانات لتعلم وظيفة المحاذاة لتوجيه نموذج اللغة غير المستخدمة مسبقا وغير الخاضعة للرقابة لإنشاء نصوص مع سمة الهدف دون تغييرالمعلمات نموذج اللغة الأصلية.نقوم بتقييم طريقتنا على توليد المعنويات والموضوع، وإظهار مكاسب أداء كبيرة على الطرق السابقة مع الاحتفاظ بالطلاقة والتنوع.
Large language models benefit from training with a large amount of unlabeled text, which gives them increasingly fluent and diverse generation capabilities. However, using these models for text generation that takes into account target attributes, such as sentiment polarity or specific topics, remains a challenge. We propose a simple and flexible method for controlling text generation by aligning disentangled attribute representations. In contrast to recent efforts on training a discriminator to perturb the token level distribution for an attribute, we use the same data to learn an alignment function to guide the pre-trained, non-controlled language model to generate texts with the target attribute without changing the original language model parameters. We evaluate our method on sentiment- and topic-controlled generation, and show large performance gains over previous methods while retaining fluency and diversity.
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