Synthetic Data-Driven Prompt Tuning for Financial QA over Tables and Documents

Overview

The paper presents a novel framework for improving financial document analysis using large language models (LLMs). By generating synthetic data, the framework enhances prompt tuning, leading to better performance in numerical reasoning tasks.

Key Insights

• Self-Improving Framework: The framework uses a closed-loop process involving synthetic data generation, verification, and prompt optimization.
• Improved Performance: Evaluation on the DocMath-Eval benchmark shows higher accuracy and robustness compared to standard methods.
• Cost-Effective: The method reduces the need for costly, manually labeled datasets.

Why It Matters

This advancement is crucial for financial services, particularly in areas requiring detailed document analysis like corporate banking and asset management.

Actionable Implications

• Consider adopting synthetic data-driven approaches for document analysis.
• Explore integrating this framework into existing financial QA systems to enhance accuracy and efficiency.