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CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications

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  • Yupeng Cao
  • Zhiyuan Yao
  • Zhi Chen
  • Zhiyang Deng

Abstract

The integration of Large Language Models (LLMs) into financial analysis has garnered significant attention in the NLP community. This paper presents our solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs within three critical areas of financial tasks: financial classification, financial text summarization, and single stock trading. We adopted Llama3-8B and Mistral-7B as base models, fine-tuning them through Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) approaches. To enhance model performance, we combine datasets from task 1 and task 2 for data fusion. Our approach aims to tackle these diverse tasks in a comprehensive and integrated manner, showcasing LLMs' capacity to address diverse and complex financial tasks with improved accuracy and decision-making capabilities.

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  • Yupeng Cao & Zhiyuan Yao & Zhi Chen & Zhiyang Deng, 2024. "CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications," Papers 2407.01953, arXiv.org.
    • Handle: RePEc:arx:papers:2407.01953
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    References listed on IDEAS

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    1. Franklin Allen & Xian Gu & Julapa Jagtiani, 2021. "A Survey of Fintech Research and Policy Discussion," Review of Corporate Finance, now publishers, vol. 1(3-4), pages 259-339, July.
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