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At the beginning era of large language model, it is quite critical togenerate a high-quality financial dataset to fine-tune a large language modelfor financial related tasks. Thus, this paper presents a carefully designeddata creation pipeline for this purpose. Particularly, we initiate a dialoguebetween an AI investor and financial expert using ChatGPT and incorporate thefeedback of human financial experts, leading to the refinement of the dataset.This pipeline yielded a robust instruction tuning dataset comprised of 103kmulti-turn chats. Extensive experiments have been conducted on this dataset toevaluate the model's performance by adopting an external GPT-4 as the judge.The promising experimental results verify that our approach led to significantadvancements in generating accurate, relevant, and financial-style responsesfrom AI models, and thus providing a powerful tool for applications within thefinancial sector.

An Effective Data Creation Pipeline to Generate High-quality Financial Instruction Data for Large Language Model