Papers by Roei Schuster
Transformer Feed-Forward Layers Are Key-Value Memories (2021.emnlp-main)
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| Challenge: | Using feed-forward layers, we show that the learned patterns are human-interpretable, and that lower layers tend to capture shallow patterns, while upper layers learn more semantic ones. |
| Approach: | They propose that feed-forward layers in transformer-based language models operate as key-value memories where each key correlates with textual patterns in the training examples and each value induces a distribution over the output vocabulary. |
| Outcome: | The proposed model is based on key-value memories with a key-level correlation with the training examples and a distribution over the output vocabulary. |
Understanding Transformer Memorization Recall Through Idioms (2023.eacl-main)
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| Challenge: | Existing methods for analyzing memorization use definitions that are based on model performance, which changes between models and often also between training runs. |
| Approach: | They propose idioms as inputs that typically trigger memory recall and propose a set of English idiomas to test their methodological framework for probing and characterizing recall of memorized sequences in transformer LMs. |
| Outcome: | The proposed framework compares model behavior on memorized vs. non-memorized inputs across different model sizes and architectures. |