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