Summary of Finllms: a Framework For Financial Reasoning Dataset Generation with Large Language Models, by Ziqiang Yuan et al.
FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models
by Ziqiang Yuan, Kaiyuan Wang, Shoutai Zhu, Ye Yuan, Jingya Zhou, Yanlin Zhu, Wenqi Wei
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models. The authors address the limited data resources and reduce manual annotation expenses by compiling a list of common financial formulas, constructing a graph based on variables, and augmenting it with new elements. They then generate financial question-answering data using GPT-3.5, which enhances the performance of several large-scale numerical reasoning models in the financial domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to train computer models that understand financial data by creating fake questions and answers based on common financial formulas. The authors use a special kind of AI called Large Language Models to generate this data. They show that using this generated data makes their computer models better at answering financial questions. This can be helpful for banks, accountants, and other organizations that need to analyze financial data. |
Keywords
» Artificial intelligence » Gpt » Question answering
