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AI Research
arXiv CS.AI·25d ago·5 min read
Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention
TL;DR
Large language models often face challenges with size and efficiency. This study introduces a structured pruning method using power transformation and sign-preserving score aggregation to enhance model performance while retaining essential features.
✦ Why It Matters
Engineers can implement structured pruning to optimize large language models for deployment in resource-limited environments today.
Key Takeaways
- Explore structured pruning to optimize large language models.
- Utilize power transformation for better score distribution alignment.
- Implement sign-preserving score aggregation to maintain optimization direction.
- Apply percentile-based outlier removal to enhance pruning effectiveness.
How It Works
The method combines power transformation to align the distribution of pruning scores, ensuring that the scores are comparable across different model components. Sign-preserving score aggregation retains the direction of optimization, which is crucial for effective pruning.
Additionally, percentile-based outlier removal mitigates the influence of extreme values that could skew the pruning process.