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مشاهدة نموذج اللغة تعلم الشطرنج
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نحن نحلل كيف يتعلم نموذج اللغة القائم على المحولات قواعد الشطرنج من البيانات النصية للألعاب المسجلة.نوضح كيف يمكن البحث عن كيفية القدرة النموذجية والعدد المتاح لبيانات التدريب التي تؤثر على نجاح تعلم نموذج اللغة بمساعدة مقاييس الشطرنج الخاصة.مع هذه المقاييس، نوضح أن المزيد من الألعاب المستخدمة للتدريب في النطاق المستخدمة تقدم نتائج أفضل بكثير لنفس وقت التدريب.ومع ذلك، فإن حجم النموذج لا يظهر مثل هذا التأثير الواضح.من المثير للاهتمام أيضا أن نلاحظ أن مقاييس التقييم المعتادة لنماذج اللغة، ودقة التنبؤية والحيرة، لا تعطي أي إشارة إلى هذا هنا.يكشف فحص المزيد من النماذج المدربة عن كيفية تخزين المعلومات حول حالة المجلس في تفعيلات مجموعات الخلايا العصبية، وكيف تؤثر التسلسل العام للحركات السابقة على التحركات التي تم إنشاؤها حديثا.
We analyse how a transformer-based language model learns the rules of chess from text data of recorded games. We show how it is possible to investigate how the model capacity and the available number of training data influence the learning success of a language model with the help of chess-specific metrics. With these metrics, we show that more games used for training in the studied range offers significantly better results for the same training time. However, model size does not show such a clear influence. It is also interesting to observe that the usual evaluation metrics for language models, predictive accuracy and perplexity, give no indication of this here. Further examination of trained models reveals how they store information about board state in the activations of neuron groups, and how the overall sequence of previous moves influences the newly-generated moves.
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