تقرير
Explaining How Transformers Use Context to Build Predictions
| العنوان: | Explaining How Transformers Use Context to Build Predictions |
|---|---|
| المؤلفون: | Ferrando, Javier, Gállego, Gerard I., Tsiamas, Ioannis, Costa-jussà, Marta R. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | Language Generation Models produce words based on the previous context. Although existing methods offer input attributions as explanations for a model's prediction, it is still unclear how prior words affect the model's decision throughout the layers. In this work, we leverage recent advances in explainability of the Transformer and present a procedure to analyze models for language generation. Using contrastive examples, we compare the alignment of our explanations with evidence of the linguistic phenomena, and show that our method consistently aligns better than gradient-based and perturbation-based baselines. Then, we investigate the role of MLPs inside the Transformer and show that they learn features that help the model predict words that are grammatically acceptable. Lastly, we apply our method to Neural Machine Translation models, and demonstrate that they generate human-like source-target alignments for building predictions. Comment: ACL 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2305.12535 |
| رقم الأكسشن: | edsarx.2305.12535 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |