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---
license: mit
task_categories:
- question-answering
- multiple-choice
language:
- en
tags:
- finance
- numerical-reasoning
- table-qa
- financial-analysis
size_categories:
- 10K<n<100K
---
# FinRAG
## Dataset Description
This dataset contains 12,500 financial reasoning questions based on real-world financial documents, earnings reports, and financial tables. Each question is accompanied by a correct answer and four carefully crafted distractor answers, making it suitable for multiple-choice question answering tasks and assessing financial numerical reasoning capabilities.
### Dataset Summary
- **Total Examples**: 12,500
- **Format**: Multiple-choice questions with 5 options (1 correct + 4 distractors)
- **Domain**: Financial documents, earnings reports, financial tables
- **Task**: Numerical reasoning over financial text and tables
- **Language**: English
### Source Data
This dataset combines all splits (train, validation, and test) from:
- **FinQA**: Financial Question Answering dataset (7,750 questions, 62%)
- **TAT-QA**: Table-and-Text Question Answering dataset (4,750 questions, 38%)
### Distractor Generation
Four distractor answers were algorithmically generated for each question using the following techniques:
- **Stop early**: Stopping calculation before completion
- **Negate operand**: Negating numbers in the calculation
- **Operand bleeding**: Using the wrong operands from the table
- **Replace operator**: Using the wrong mathematical operation (e.g., multiply instead of add)
- **Switch order**: Changing the order of operations
- **Percentage error**: Mistakes in percentage conversion
- **Unit error**: Mistakes in unit conversion (e.g., millions vs. thousands)
- **Append operation**: Adding extra unnecessary operations
- **Substitution error**: Substituting incorrect values from the table
These techniques create plausible but incorrect answers that test true understanding of the financial reasoning task.
## Dataset Structure
### Data Fields
Each example in the dataset contains:
- **`id`** (string): Unique identifier for each question
- **`pre_text`** (list of strings): Contextual text passages from the financial document that appear before the table
- **`post_text`** (list of strings): Additional contextual text passages that appear after the table (may be empty)
- **`table`** (list of lists): Financial table data in row-major format, where the first row typically contains headers
- **`question`** (string): The financial reasoning question to be answered
- **`choices`** (list of strings): List of 5 answer choices (1 correct + 4 distractors), randomly shuffled
- **`answer`** (integer): Index (0-4) pointing to the correct choice in the `choices` list
### Data Example
```json
{
"id": "9bbb9fb3-3482-4d4d-be40-dd6ff47c23e9",
"pre_text": [
"Orders at Mobility grew to a record high on a sharp increase in volume...",
"Revenue grew slightly as double-digit growth in the customer services business..."
],
"post_text": [],
"table": [
["", "", "Fiscal year", "", "% Change"],
["(in millions of €)", "2019", "2018", "Actual", "Comp."],
["Orders", "12,894", "11,025", "17 %", "16 %"],
["Revenue", "8,916", "8,821", "1 %", "0 %"]
],
"question": "Analyse this data from a financial earnings document. What it the increase / (decrease) in revenue from 2018 to 2019?",
"choices": ["-3978", "17737", "94", "95", "1"],
"answer": 3
}
```
## Dataset Sources and Attribution
This derived dataset is based on two source datasets that are licensed under **Creative Commons Attribution 4.0 International (CC-BY 4.0)**. The distractor generation methodology and dataset structure are licensed under MIT. This work complies with the CC-BY 4.0 attribution requirements by providing proper attribution, copyright notices, license information, and links to the original datasets below.
### FinQA Dataset
**Citation:**
```bibtex
@inproceedings{chen-etal-2021-finqa,
title = "{F}in{QA}: A Dataset of Numerical Reasoning over Financial Data",
author = "Chen, Zhiyu and Chen, Wenhu and Smiley, Charese and Shah, Sameena and
Borova, Iana and Langdon, Dylan and Moussa, Reema and Beane, Matt and
Huang, Ting-Hao and Routledge, Bryan and Wang, William Yang",
booktitle = "Proceedings of EMNLP 2021",
year = "2021"
}
```
**License:** [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
**Original Dataset:** https://finqasite.github.io/
**Modifications:** This derived work adds multiple-choice distractors to the original FinQA questions.
### TAT-QA Dataset
**Citation:**
```bibtex
@inproceedings{zhu-etal-2021-tat,
title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance",
author = "Zhu, Fengbin and Lei, Wenqiang and Wang, Chao and Zheng, Jianming and
Poria, Soujanya and Chua, Tat-Seng",
booktitle = "Proceedings of ACL-IJCNLP 2021",
year = "2021"
}
```
**License:** [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
**Original Dataset:** https://nextplusplus.github.io/TAT-QA/
**Modifications:** This derived work adds multiple-choice distractors to the original TAT-QA questions. |