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Register a new userAutomatically Exposing Problems with Neural Dialog Models
تعريض مشاكل تلقائيا مع نماذج الحوار العصبية
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من المعروف أن نماذج الحوار العصبي تعاني من مشاكل مثل توليد ردود غير آمنة وغير متناسقة.على الرغم من أن هذه المشاكل حاسمة وسائدة، إلا أنها تعد في الغالب يدويا من قبل مصممي النماذج من خلال التفاعلات.في الآونة الأخيرة، يرشد بعض الأبحاث أن الجماعات الجمهورية إلى البوتات الروبوتين في إثارة مثل هذه المشاكل.ومع ذلك، فإن البشر يستفيدون من القرائن السطحية مثل خطاب الكراهية، مع ترك المشاكل المنهجية السرية.في هذه الورقة، نقترح طريقتان بما في ذلك التعزيز التعلم لتشغيل نموذج حوار تلقائيا إلى استجابات إشكالية.نظهر تأثير طرقنا في فضح مشاكل السلامة والتناقض مع نماذج الحوار الحديثة.
Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly manually identified by model designers through interactions. Recently, some research instructs crowdworkers to goad the bots into triggering such problems. However, humans leverage superficial clues such as hate speech, while leaving systematic problems undercover. In this paper, we propose two methods including reinforcement learning to automatically trigger a dialog model into generating problematic responses. We show the effect of our methods in exposing safety and contradiction issues with state-of-the-art dialog models.
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