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.

本文研究了大型语言模型在金融分析中的应用，通过 IJCAI-2024 FinLLM 挑战模拟了金融分类、金融文本摘要和个股交易等关键任务，并采用 Llama3-8B 和 Mistral-7B 作为基本模型，通过 Parameter Efficient Fine-Tuning（PEFT）和 Low-Rank Adaptation（LoRA）方法进行微调，将任务一和任务二的数据集进行融合，综合和全面地解决了这些多样且复杂的金融任务，展示了大型语言模型在提高准确性和决策能力方面的潜力。