# TensorGPT: Efficient Compression of the Embedding Layer in LLMs based on the Tensor-Train Decomposition

**Authors:** Mingxue Xu, Yao Lei Xu, Danilo P. Mandic  
**arXiv:** [2307.00526](https://arxiv.org/abs/2307.00526)  
**Date:** 2023-07-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.00526v1/

## Key points

- High-dimensional token embeddings are crucial for capturing nuanced semantic information and improving complex language pattern modeling in Large Language Models (LLMs).
- The increased dimensionality poses challenges in terms of storage requirements and model parameters.
- A novel approach based on Tensor-Train Decomposition (TTD) is proposed to address the issue, treating each token embedding as a Matrix Product State (MPS) for efficient distributed computation.
- Experimental results on GPT-2 show impressive compression factors of up to 38.40 times for the embedding layer, with even a low compression factor of 3.31 times resulting in outperformance compared to the original GPT-2 model.
- The study "TensorGPT" by Mingxue Xu, Yao Lei Xu, and Danilo P. Mandic demonstrates how advancements in tensor decomposition techniques can significantly improve efficiency and performance in large-scale language models.
- Leveraging TTD for compressing token embeddings successfully addresses the challenge of high dimensionality while maintaining or surpassing performance levels of existing models like GPT-2.
- This research contributes to enhancing scalability and practicality of LLMs, opening up new possibilities for further exploration and optimization using advanced mathematical frameworks such as tensor decomposition methods.

## Summary

The role of high-dimensional token embeddings in Large Language Models (LLMs) is crucial for capturing nuanced semantic information and improving the modeling of complex language patterns. However, the increased dimensionality also presents challenges in terms of storage requirements and model parameters. To address this issue, a novel approach based on Tensor-Train Decomposition (TTD) is proposed in this work. Each token embedding is viewed as a Matrix Product State (MPS), allowing for efficient computation in a distributed manner. The experimental results on GPT-2 demonstrate the effectiveness of this approach, with an impressive compression factor of up to 38.40 times for the embedding layer. Even with a compression factor as low as 3.31 times, the TTD-based model outperformed the original GPT-2 model. This study titled "TensorGPT: Efficient Compression of the Embedding Layer in LLMs based on the Tensor-Train Decomposition" by Mingxue Xu, Yao Lei Xu, and Danilo P. Mandic highlights how advancements in tensor decomposition techniques can lead to significant improvements in efficiency and performance within large-scale language models. By leveraging TTD to compress token embeddings, researchers have successfully addressed the challenge posed by high dimensionality while maintaining or even surpassing the performance levels of existing models like GPT-2. This research not only contributes to enhancing scalability and practicality of LLMs but also opens up new possibilities for further exploration and optimization using advanced mathematical frameworks such as tensor decomposition methods. The findings pave the way for more streamlined and resource-efficient implementations of state-of-the-art language models, opening up new possibilities for advancing natural language processing technologies.