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.

翻译：大语言模型在金融分析中的应用已受到自然语言处理领域的广泛关注。本文介绍了我们在IJCAI-2024 FinLLM挑战赛中的解决方案，探究了大语言模型在金融任务三个关键领域的能力：金融文本分类、金融文本摘要与单股票交易预测。我们采用Llama3-8B和Mistral-7B作为基础模型，通过参数高效微调与低秩自适应方法进行模型优化。为提升模型性能，我们将任务1与任务2的数据集进行融合处理。该方法旨在以全面整合的方式处理这些多样化任务，展示了大语言模型通过提升准确性与决策能力应对复杂金融任务的潜力。