README.md

---
language:
- ru
license: mit
size_categories:
- 10K<n<100K
task_categories:
- question-answering
- text-generation
- table-question-answering
pretty_name: RuFinQA-CoT
tags:
- russian
- finance
- llm-evaluation
- chain-of-thought
- benchmarking
- reasoning
- cot
- financial-analysis
---

# 🏦 RuFinQA-CoT

**RuFinQA-CoT** is a Russian benchmark for evaluating Large Language Models (LLMs) on financial analysis tasks with **Ground-Truth Chain-of-Thought**.

## 📊 Overview

- **Total questions:** 44,627
- **Task types:** 7
- **Skills:** 12
- **Difficulty levels:** 3
- **Version:** 3.3.0
- **Language:** Russian

## 🎯 Task Types

| Type | Count | Share | Description |
|------|-------|-------|-------------|
| `factoid` | 12,255 | 27.5% | Fact extraction from company data |
| `arithmetic` | 12,021 | 26.9% | Financial metric calculation (ROA, ROE, CR) |
| `multistep` | 7,799 | 17.5% | Multi-step reasoning |
| `comparison` | 4,085 | 9.2% | Year-over-year metric comparison |
| `analytical` | 4,059 | 9.1% | Financial metric interpretation |
| `reasoning` | 4,052 | 9.1% | Capital structure analysis (leverage) |
| `adversarial` | 356 | 0.8% | Robustness tests against anomalies |

## 📈 Difficulty Distribution

| Level | Count | Share |
|-------|-------|-------|
| `hard` | 16,292 | 36.5% |
| `medium` | 16,080 | 36.0% |
| `easy` | 12,255 | 27.5% |

## 🔗 Chain-of-Thought Structure

Each question contains annotated reasoning steps:

```json
{
  "reasoning_steps": [
    {
      "step": 1,
      "description": "Extract values from the financial report",
      "intermediate_result": "bal_1600 = 633.696, inc_2400 = 44.691",
      "is_critical": true
    },
    {
      "step": 2,
      "description": "Apply the ROA formula",
      "intermediate_result": "ROA = 44.691 / 633.696",
      "is_critical": true
    },
    {
      "step": 3,
      "description": "Compute the result",
      "intermediate_result": "ROA = 0.0705 (7.05%)",
      "is_critical": true
    }
  ],
  "cot_text": "Step 1: ... → ...\nStep 2: ... → ...\n\nAnswer: ...",
  "final_answer": "ROA = 44.691 / 633.696 = 0.0705 (7.05%)"
}
```

## 📝 Data Structure

Each record contains:

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique question identifier |
| `type` | string | Task type (arithmetic/factoid/...) |
| `skill` | string | Evaluated skill |
| `difficulty` | string | Difficulty level (easy/medium/hard) |
| `question` | string | Question in Russian |
| `context` | dict | Context (TIN, year, financial data) |
| `reasoning_steps` | list | Ground-Truth reasoning steps |
| `cot_text` | string | Textual representation of CoT |
| `final_answer` | string | Final answer |
| `numeric_answer` | float | Numeric answer (for arithmetic) |
| `tolerance` | float | Tolerance for numeric evaluation |
| `evaluation` | string | Evaluation metric type |

## 🚀 Usage

### Loading the Benchmark

```python
from datasets import load_dataset

dataset = load_dataset("arabovs-ai-lab/RuFinQA-CoT", split="train")
print(f"Total questions: {len(dataset)}")
print(dataset[0])
```

### Example Usage for LLM Evaluation

```python
import json
from datasets import load_dataset

dataset = load_dataset("arabovs-ai-lab/RuFinQA-CoT", split="train")

# Filter by task type
arithmetic_tasks = [d for d in dataset if d['type'] == 'arithmetic']
print(f"Arithmetic tasks: {len(arithmetic_tasks)}")

# Sample question
sample = arithmetic_tasks[0]
print(f"Question: {sample['question']}")
print(f"Expected answer: {sample['final_answer']}")
print(f"CoT:\n{sample['cot_text']}")
```

### Model Evaluation

```python
# Simple evaluation example
def evaluate_numeric(prediction: str, ground_truth: float, tolerance: float) -> bool:
    import re
    numbers = [float(x) for x in re.findall(r'-?\d+\.?\d*', prediction)]
    return any(abs(n - ground_truth) <= tolerance for n in numbers)

# Check model output
model_output = "ROA = 0.0705 (7.05%)"
is_correct = evaluate_numeric(model_output, 0.0705, 0.001)
print(f"Answer is correct: {is_correct}")
```

## 🤖 Evaluated Models

_Model evaluations will be added in future releases._

## 🔬 Benchmark Features

1. **Ground-Truth CoT**: Each question contains annotated reasoning steps with an `is_critical` flag for critical steps
2. **Stratification**: Questions are stratified by type, skill, and difficulty
3. **Metadata**: Complete information about version, distribution, and metrics
4. **Multiple evaluation types**: `cot_numeric`, `cot_text`, `cot_exact`, `cot_keyword`, `cot_reasoning`
5. **Adversarial examples**: Robustness tests against negative values and anomalies
6. **Realistic data**: Anonymized financial reports of Russian companies

## 📚 Applications

- Evaluating LLM reasoning capabilities
- CoT distillation for financial tasks
- Model comparison on Russian language
- Investigating the impact of difficulty on answer quality
- Testing robustness against anomalous data

## 📄 License

MIT

## 📚 Citation

If you use this dataset in your work, please cite:

```bibtex
@misc{rufinqacot2026,
  author = {Arabov, Mullosharaf K.},
  title = {RuFinQA-CoT: A Russian Financial Benchmark with Ground-Truth Chain-of-Thought for LLM Evaluation},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/arabovs-ai-lab/RuFinQA-CoT}},
}
```

## 👤 Author

**Mullosharaf K. Arabov**  
PhD in Physics and Mathematics, Associate Professor  
Department of Data Analysis and Programming Technologies  
Kazan (Volga Region) Federal University

## 🔗 Links

- 📊 Dataset: https://huggingface.co/datasets/arabovs-ai-lab/RuFinQA-CoT
- 💻 Generator code: [GitHub]
- 📝 Paper: [arXiv]

---

*Automatically generated from the RuFinQA-CoT evaluation pipeline.*