Papers by Peter Ramadge
Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation (2023.findings-emnlp)
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| Challenge: | Existing models of working memory are limited by self-attention, but we propose a new variant of regular programming that can model regular languages. |
| Approach: | They propose a Transformer variant that constructs working memory along the depth dimension . they find it rediscovers the local windowed attention effect needed for length extrapolation . |
| Outcome: | The proposed model rediscovers the local windowed attention effect needed for length extrapolation. |
Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings (2023.acl-short)
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| Challenge: | Recent research has called into question the necessity of positional embeddings in transformer language models. |
| Approach: | They propose to discard positional embeddings in transformer language models to facilitate more efficient pretraining. |
| Outcome: | The proposed model encodes strong positional information through shrinkage of self-attention variance. |
Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysis (2023.acl-long)
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| Challenge: | Length extrapolation allows training a transformer language model on short sequences that preserves perplexities when tested on substantially longer sequences. |
| Approach: | They propose a relative positional embedding design that uses longer than the training sequence to create sandwich. |
| Outcome: | The proposed model can extrapolate to L ex L tr much better than other models. |