# Methodology

Although pre-trained language models like GPT-3 and LLaMA can acquire general abilities to solve various tasks, a growing body of research shows that the capabilities of these language models can be further adjusted to specific goals. Our approach uses instruction tuning to fit a generic language model for financial sentiment analysis, enhancing its ability to understand values and contexts. The process involves transforming sentiment analysis tasks from classification tasks to text generation tasks, which aligns more with language models[41-43]. In addition, we use the transformed data set to fine-tune the language model with instructions in a supervised learning manner. Finally, the generated output is mapped to emotional labels during inference.

## Command Adjustment

We use a language model-based instruction adjustment approach to process financial sentiment analysis datasets. The process is divided into three main steps: Formatting financial Sentiment analysis datasets into instruction-adjusting datasets. Existing financial sentiment analysis datasets are formatted into text classification tasks. [44]The input is financial news or headlines, and the output is integer-type labels representing positive, negative, and neutral emotions. Our first step is to convert these classification datasets into datasets in instruction format. Based on the method, we create 10 human-written instructions describing the financial sentiment analysis task by combining a randomly selected instruction with input and output into a sample with the format ”Human: [instruction] + [input], assistant: [output]”. This process is shown in Figure 1.