LLMs must not only be accurate - they must be efficient enough to operate where it matters

Our latest publication in Complex & Intelligent Systems presents a structured and up-to-date overview of model compression strategies tailored to large language models (LLMs): https://lnkd.in/eZjwgUF6.

This work is particularly valuable for researchers and practitioners aiming to:
- Understand the landscape of compression methods (pruning, quantization, distillation, NAS).
- Explore hardware-aware and fairness-driven design trade-offs.
- Apply a multi-objective evaluation framework (latency, energy, accuracy, robustness).
- Gain insight into hybrid and adaptive approaches for real-world deployment.
- Navigate open challenges and research directions through a detailed roadmap.

Readers looking for practical guidance and theoretical depth will find this review a useful reference point for both academic study and applied development.

Special thanks to my advisor and co-authors, Prof. Waldir Sabino and Prof. Lucas Cordeiro, for their guidance and collaboration throughout this project. Research group and contributors: https://lnkd.in/exKf_wFP

In upcoming work, we will delve into spectral analysis of LLMs, aiming to uncover new compression and interpretability methods rooted in frequency-domain representations.

We also invite you to explore our previous publication on hybrid adaptive compression methods: https://lnkd.in/eyuirZ6R