Summary of Finsql: Model-agnostic Llms-based Text-to-sql Framework For Financial Analysis, by Chao Zhang et al.
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis
by Chao Zhang, Yuren Mao, Yijiang Fan, Yu Mi, Yunjun Gao, Lu Chen, Dongfang Lou, Jinshu Lin
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 The paper proposes a novel Large Language Model (LLM)-based framework for financial text-to-SQL applications, which provides a zero-code interface for operating relational databases without SQL programming skills. The authors introduce the BULL benchmark dataset, collected from Hundsun Technologies Inc.’s practical financial analysis business, featuring wide tables common in finance. They also present FinSQL, a model-agnostic framework that addresses prompt construction, parameter-efficient fine-tuning, and output calibration challenges specific to financial Text-to-SQL tasks. Experimental results demonstrate state-of-the-art performance on BULL, with up to 36.64% improvement in few-shot cross-database scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps computers understand SQL commands without needing programming skills. This is important for financial professionals who need to analyze data but don’t know how to write code. The researchers created a special dataset and a new way to approach this problem using large language models. They tested their method on real-world financial data and found it worked well, even when learning from one database and then applying that knowledge to another. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Large language model » Parameter efficient » Prompt
