Summary of Quanta: Efficient High-rank Fine-tuning Of Llms with Quantum-informed Tensor Adaptation, by Zhuo Chen et al.
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
by Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantum Physics (quant-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach called Quantum-informed Tensor Adaptation (QuanTA) is proposed for fine-tuning large-scale pre-trained language models. This method leverages quantum-inspired techniques to enable efficient high-rank fine-tuning, surpassing the limitations of Low-Rank Adaptation (LoRA). QuanTA’s theoretical foundations are rooted in the universality theorem and rank representation theorem, ensuring efficient adaptations. The approach is experimentally shown to enhance commonsense reasoning, arithmetic reasoning, and scalability compared to traditional methods. Additionally, QuanTA requires fewer trainable parameters than other approaches and can be integrated with existing fine-tuning algorithms for further improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary QuanTA is a new way to improve language models without using too much extra information or processing power. It works by taking ideas from quantum computers and applying them to large language models, making it easier to adapt these models to specific tasks. This helps the models do better on things like reasoning and math problems. It also makes it possible to use fewer parameters, which can help with training and using the models. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation
