Enhancing Large Language Models (LLMs) with Efficient Compression Techniques
Understanding the Challenge
Large Language Models (LLMs) like GPT and LLaMA are powerful due to their complex structures and extensive training. However, not all parts of these models are necessary for good performance. This has led to the need for methods that make these models more efficient without losing quality.
Practical Solutions
The LASER model is one example that reduces unnecessary weight in its networks using a method called Singular Value Decomposition (SVD). While effective, it only focuses on individual weight matrices and misses out on shared information.
A New Approach from Imperial College London
Researchers have developed a new framework that improves LLM reasoning by compressing the Multi-Head Attention (MHA) block. This method uses multi-head tensorisation and Tucker decomposition, allowing for significant compression—up to 250 times—without needing extra data or fine-tuning. It enhances the model’s reasoning by utilizing the shared roles of attention heads.
Technical Insights
This framework reshapes MHA weight matrices into 3D tensors, which helps in better data representation and reduces noise. By ensuring that all attention heads work in the same higher-dimensional space, the model’s reasoning capability is improved.
Proven Results
Tests on various benchmark datasets with models like RoBERTa, GPT-J, and LLaMA2 showed that this method significantly enhances reasoning while compressing parameters. It works well with existing compression methods and often outperforms them when combined.
Conclusion and Future Directions
This new framework not only boosts reasoning in LLMs but also achieves impressive parameter compression. By using advanced techniques, it enhances model efficiency without requiring additional training. Future work will focus on making this approach applicable across different datasets.
Get Involved
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