CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications
<|reference_start|>CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications: 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.<|reference_end|>
@article{cao2024catmemo,
    title={CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models
  using Data Fusion in Financial Applications},
    author={Yupeng Cao, Zhiyuan Yao, Zhi Chen, Zhiyang Deng},
    journal={arXiv preprint arXiv:2407.01953},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01953},
    primaryClass={cs.CE cs.AI cs.LG q-fin.CP}
}