README.md

---
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.