SEC-QA: A Systematic Evaluation Corpus for Financial QA
SEC-QA framework generates financial multi-document QA datasets, enhancing model evaluation accuracy.
Key Findings
Methodology
The SEC-QA framework semi-automatically generates QA pairs across multiple long-context financial documents and regularly updates the dataset using the latest public document collections. It leverages program-of-thought to improve complex information retrieval and quantitative reasoning capabilities.
Key Results
- Experiments show traditional retrieval augmented generation methods systematically fail to answer complex multi-document questions.
- CodeGen+DocS+PageR system performs best in multi-document QA tasks, achieving 80% accuracy.
- Document selection step significantly improves retrieval performance, enhancing QA accuracy.
Significance
The SEC-QA framework addresses the scarcity of datasets in the financial domain, providing a dynamically generated benchmark that prevents performance inflation due to data leaks. It offers a more reliable evaluation tool for automating financial data analysis.
Technical Contribution
The SEC-QA framework fundamentally differs from existing methods in its ability to dynamically generate and update datasets, and its innovative approach to complex information retrieval using program-of-thought.
Novelty
SEC-QA is the first framework to provide dynamic generation and updating of financial multi-document QA datasets, addressing evaluation biases caused by static datasets.
Limitations
- The current framework still faces challenges in handling complex compound metrics, with some metrics not directly extractable from documents.
- Document selection step relies on accurate meta-information, which may lead to misretrieval.
Future Work
Future research directions include further optimizing program-of-thought methods to improve compound metric extraction accuracy and exploring more financial application scenarios.
AI Executive Summary
The financial domain often deals with large numbers of long documents, and existing datasets fail to accurately reflect real-world tasks, making model evaluation challenging. The SEC-QA framework semi-automatically generates QA pairs across multiple long-context financial documents and regularly updates the dataset using the latest public document collections. Experiments show traditional retrieval augmented generation methods systematically fail to answer complex multi-document questions. The CodeGen+DocS+PageR system performs best in multi-document QA tasks, achieving 80% accuracy. The SEC-QA framework addresses the scarcity of datasets in the financial domain, providing a dynamically generated benchmark that prevents performance inflation due to data leaks. Future research directions include further optimizing program-of-thought methods to improve compound metric extraction accuracy and exploring more financial application scenarios.
Deep Analysis
Background
Automating financial data analysis has been a significant research direction. Existing financial QA datasets like FinQA and TAT-QA provide a foundation but fail to meet the demands of complex multi-document tasks. The SEC-QA framework aims to address this issue by dynamically generating datasets to improve evaluation accuracy.
Core Problem
Model evaluation in the financial domain faces challenges due to dataset scarcity and static nature, leading to unreliable evaluation results. Existing datasets fail to reflect real financial scenarios, impacting model applicability.
Innovation
The SEC-QA framework semi-automatically generates QA pairs across multiple long-context financial documents and regularly updates the dataset using the latest public document collections. This dynamic generation and updating capability is not present in existing methods.
Methodology
- �� Utilize program-of-thought methods to enhance complex information retrieval capabilities.
- �� Regularly update datasets using the latest public document collections.
- �� Semi-automatically generate QA pairs to ensure dataset authenticity and diversity.
Experiments
Experimental design includes evaluating four different QA systems using complex financial question sets generated by SEC-QA. Evaluation metrics include document retrieval accuracy, page retrieval accuracy, and QA accuracy.
Results
CodeGen+DocS+PageR system performs best in multi-document QA tasks, achieving 80% accuracy. Document selection step significantly improves retrieval performance, enhancing QA accuracy.
Applications
The SEC-QA framework can be used as an evaluation tool for automating financial data analysis, helping financial institutions improve analysis efficiency and accuracy.
Limitations & Outlook
The current framework still faces challenges in handling complex compound metrics, with some metrics not directly extractable from documents. Document selection step relies on accurate meta-information, which may lead to misretrieval.
Plain Language Accessible to non-experts
Imagine you're in a library looking for a book that contains the information you need. SEC-QA is like a smart librarian who not only knows where the book is but can also help you find the most relevant chapters within it. It can continuously update the library to ensure you always find the latest information.
ELI14 Explained like you're 14
Imagine you're playing a treasure hunt game, and the game has many clues hidden in different places. SEC-QA is like a super helper that can find all the clues and tell you how to combine them to find the treasure. It can also keep updating the game so you always have new challenges!
Glossary
SEC-QA (Systematic Evaluation Corpus)
A dataset generation framework for financial multi-document QA tasks.
Used to generate dynamically updated financial QA datasets.
Program-of-Thought
A method using program logic for complex information retrieval.
Used to enhance retrieval capabilities in financial QA systems.
Retrieval Augmented Generation (RAG)
A method that enhances generation models by retrieving relevant documents.
Used to compare traditional QA methods with SEC-QA performance.
Compound Metric
Complex financial metrics composed of multiple basic metrics.
Used to test SEC-QA's performance in complex metric extraction tasks.
Document Selection
A step that filters relevant documents based on meta-information.
Used to improve retrieval accuracy.
Open Questions Unanswered questions from this research
- 1 How to further improve compound metric extraction accuracy?
- 2 How to optimize program-of-thought methods for more financial application scenarios?
Applications
Immediate Applications
Automated Financial Data Analysis
SEC-QA framework can be used to evaluate financial data analysis tools, improving analysis efficiency.
Long-term Vision
Dynamic Financial Dataset Generation
SEC-QA framework can be used to create dynamically updated financial datasets, supporting long-term research.
Abstract
The financial domain frequently deals with large numbers of long documents that are essential for daily operations. Significant effort is put towards automating financial data analysis. However, a persistent challenge, not limited to the finance domain, is the scarcity of datasets that accurately reflect real-world tasks for model evaluation. Existing datasets are often constrained by size, context, or relevance to practical applications. Moreover, LLMs are currently trained on trillions of tokens of text, limiting access to novel data or documents that models have not encountered during training for unbiased evaluation. We propose SEC-QA, a continuous dataset generation framework with two key features: 1) the semi-automatic generation of Question-Answer (QA) pairs spanning multiple long context financial documents, which better represent real-world financial scenarios; 2) the ability to continually refresh the dataset using the most recent public document collections, not yet ingested by LLMs. Our experiments show that current retrieval augmented generation methods systematically fail to answer these challenging multi-document questions. In response, we introduce a QA system based on program-of-thought that improves the ability to perform complex information retrieval and quantitative reasoning pipelines, thereby increasing QA accuracy.