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Register a new userEliciting Bias in Question Answering Models through Ambiguity
إظهار التحيز في سؤال الرد النماذج من خلال الغموض
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نماذج الإجابة على الأسئلة (QA) تستخدم أنظمة المسترد والقارئ للإجابة على الأسئلة.يمكن الاعتماد على البيانات التدريبية من قبل أنظمة ضمان الجودة أو تعكس عدم المساواة من خلال ردودهم.يتم تدريب العديد من نماذج QA، مثل تلك الخاصة ب DataSet Squad، على مجموعة فرعية من مقالات ويكيبيديا التي تشفص لتحيزاتها الخاصة وإعادة إنتاج عدم المساواة في العالم الحقيقي.فهم كيفية تأثير البيانات التدريبية على التحيز في أنظمة ضمان الجودة يمكن أن تبلغ الأساليب لتخفيف عدم المساواة.نقوم بتطوير مجموعتين من الأسئلة لأسئلة النطاق المغلقة والفتوة على التوالي، والتي تستخدم أسئلة غامضة لتحقيق نماذج QA للتحيز.نطعم ثلاثة أنظمة ضمان الجودة في التعلم العميق مع مجموعات الأسئلة الخاصة بنا وتقييم الردود على التحيز عبر المقاييس.باستخدام مقاييسنا، نجد أن نماذج QA المجال المفتوحة تضخيم التحيزات أكثر من نظيرهم المغلقة من النطاق واقتراح أن يتحيزات في سطح المسترد بسهولة أكبر بسبب حرية الاختيار أكبر.
Question answering (QA) models use retriever and reader systems to answer questions. Reliance on training data by QA systems can amplify or reflect inequity through their responses. Many QA models, such as those for the SQuAD dataset, are trained and tested on a subset of Wikipedia articles which encode their own biases and also reproduce real-world inequality. Understanding how training data affects bias in QA systems can inform methods to mitigate inequity. We develop two sets of questions for closed and open domain questions respectively, which use ambiguous questions to probe QA models for bias. We feed three deep-learning-based QA systems with our question sets and evaluate responses for bias via the metrics. Using our metrics, we find that open-domain QA models amplify biases more than their closed-domain counterparts and propose that biases in the retriever surface more readily due to greater freedom of choice.
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