Structured Pruning for Large Language Models Using Coupled Components Elimination and Minor Fine-tuning (2024.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated powerful capabilities in natural language processing, yet their vast number of parameters poses challenges for deployment and inference efficiency. |
| Approach: | They propose a structured pruning algorithm that derives the importance of different components based on intermediate data dependencies and removes coupled components across different layers simultaneously. |
| Outcome: | The proposed algorithm reduces model size and accelerates inference without specialized operators and libraries, while maintaining its utility as versatile problem solvers. |
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| Challenge: | minimizing reconstruction error is not always ideal and can overfit calibration data. |
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