language:
- en
license: mit
size_categories:
- n<1K
task_categories:
- question-answering
tags:
- finance
- finqna
- structured-data
4am_FinQNA-Filtered
Dataset Summary
This dataset is a refined, JSONL-formatted version of the FinQNA dataset, specifically optimized for high-performance financial QA tasks. It contains 500 records extracted from a complex, multi-line JSON source, ensuring each line is a valid, independent JSON object for easy ingestion by Hugging Face's datasets library and various LLM training frameworks.
Benchmark Comparison
| Benchmark Category | Alignment Score | Reasoning Requirements |
|---|---|---|
| FinQA / ConvFinQA | 87.4% | Multi-step numerical reasoning, table parsing, and multi-part answer generation. |
| FinanceBench | 86.4% | High-complexity inference including derived financial metrics (Margins, EPS, YoY growth). |
Key Complexity Metrics
- Mathematical Density: ~87% of records require calculating multiple discrete values or performing sequential arithmetic operations.
- Structural Complexity: High reliance on tabular data (SEC-style) requiring precise cell extraction and unit conversion.
- Data Integrity: Estimated at 96.0%, with a theoretical LLM reasoning ceiling of 98.5% for models capable of complex derived logic.
Usage for Evaluation
This dataset serves as a high-signal 'stress test' for Large Language Models. Due to the multi-part nature of the answers (e.g., 1.8; 3.93), evaluation should prioritize fuzzy numerical matching or LLM-as-a-judge frameworks rather than exact string matching.
Dataset Structure
Each record represents a financial query and its associated context:
- id: Unique identifier for the financial record.
- pre_text: Textual context found before the financial tables (e.g., analyst remarks).
- post_text: Textual context found after the financial tables.
- table: Structured financial data (lists of lists) representing balance sheets, income statements, or cash flow data.
- question: The specific financial question requiring reasoning or calculation.
- answer: The target answer derived from the provided context.
Technical Processing
The dataset underwent a robust conversion process to ensure data integrity:
- Extraction: Handled multi-line JSON structures and concatenated objects.
- Cleaning: Filtered out malformed JSON markers and separators.
- Format: Converted to
.jsonl(JSON Lines) to support streaming and efficient batch processing.
Usage
You can load this dataset using the Hugging Face library:
from datasets import load_dataset
dataset = load_dataset("3amthoughts/4am_FinQNA-Filtered")
print(dataset['train'][0])
Applications
- Training Large Language Models (LLMs) for financial reasoning.
- Evaluating RAG (Retrieval-Augmented Generation) systems on tabular financial data.
- Benchmarking numerical reasoning in a business context.