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

TL;DR

This paper introduces a data creation pipeline that uses AI-human collaboration to generate a high-quality, large-scale financial instruction dataset for fine-tuning large language models, improving their financial response capabilities.

Contribution

The paper presents a novel pipeline combining ChatGPT and human expert feedback to produce a large, high-quality financial dataset for model fine-tuning, which was not previously available.

Findings

Generated a 103k multi-turn chat dataset for financial tasks

Model responses improved in accuracy and relevance

External GPT-4 evaluation confirms effectiveness

Abstract

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.