Papers with Kronecker decomposition
Kronecker Decomposition for GPT Compression (2022.acl-short)
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| Challenge: | GPT is an auto-regressive Transformer-based pre-trained language model . but its huge size can be prohibitive for deploying on low capacity devices . |
| Approach: | They use a Kronecker decomposition technique to compress GPT models . they use ILKD to refine the model on downstream tasks . |
| Outcome: | The proposed model outperforms the existing DistilGPT2 model on language modeling and general language understanding evaluation benchmark tasks. |
KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation (2022.naacl-main)
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| Challenge: | a recent study shows that over-parameterized pre-trained language models are unsuitable for low-capacity devices. |
| Approach: | They propose a transformer-based pre-trained language model that is overparameterized . they use a two-stage knowledge distillation scheme to train the model . |
| Outcome: | The proposed model outperforms state-of-the-art models on well-known NLP benchmarks. |