Summary of Findabench: Benchmarking Financial Data Analysis Ability Of Large Language Models, by Shu Liu et al.
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models
by Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Qingquan Wu, Chong Yang
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 FinDABench, a comprehensive benchmark designed to evaluate Large Language Models (LLMs) in financial data analysis. The authors assess LLMs across three dimensions: Foundational Ability, Reasoning Ability, and Technical Skill. They test the models’ ability to perform financial numerical calculations, comprehend textual information, and analyze abnormal financial reports. The FinDABench benchmark aims to provide a measure for in-depth analysis of LLM abilities and foster the advancement of LLMs in financial data analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about testing how well big language models can do financial analysis tasks. It makes a special test called FinDABench that looks at three things: basic math skills, understanding text, and doing complex analysis. The test helps figure out how good these models are at finance stuff like calculations and looking at reports. |
