Summary of Finlora: Finetuning Quantized Financial Large Language Models Using Low-rank Adaptation, by Dannong Wang et al.
FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation
by Dannong Wang, Daniel Kim, Bo Jin, Xingjian Zhao, Tianfan Fu, Steve Yang, Xiao-Yang Liu
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers develop a novel approach to fine-tune large language models (LLMs) for financial tasks, such as sentiment analysis and information retrieval. The proposed method, quantized low-rank adaptation (QLoRA), leverages matrix decomposition and quantization techniques to reduce computational requirements while maintaining high model performance. QLoRA also enables data and pipeline parallelism, allowing for local finetuning on widely accessible GPUs. Experiments on financial datasets demonstrate substantial improvements in accuracy, GPU memory usage, and time efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be fine-tuned to perform well on specific tasks like analyzing financial news or finding relevant information. To do this locally without sharing sensitive data, researchers need ways to reduce the computational power required. This paper presents a new method called quantized low-rank adaptation (QLoRA) that makes it possible to fine-tune these models using affordable GPUs. The results show that QLoRA leads to better performance and faster processing times while still being efficient with computer resources. |
Keywords
* Artificial intelligence * Low rank adaptation * Quantization
