Papers with compression paradigm
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. |
| Approach: | They propose a new compression paradigm that extracts knowledge from pre-trained language models to construct a knowledge store from which the model can leverage it for effective inference. |
| Outcome: | The proposed model extracts knowledge from LLMs to construct a knowledge store, which the model can leverage for effective inference. |
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning (2025.findings-acl)
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| Challenge: | Weight-only quantization reduces model size but suffers from performance degradation at lower bit widths. |
| Approach: | They propose a weight-only quantization paradigm that clusters weight matrices into codebooks and finetunes them block-by-block. |
| Outcome: | The proposed paradigm outperforms quantization methods and fine tunes LLMs to 1-bit compression and fine tuning. |