Datasets:

RusNLPWorld
/

RuFinQA-CoT

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:

{
  "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

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

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

# 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

Ground-Truth CoT: Each question contains annotated reasoning steps with an is_critical flag for critical steps
Stratification: Questions are stratified by type, skill, and difficulty
Metadata: Complete information about version, distribution, and metrics
Multiple evaluation types: cot_numeric, cot_text, cot_exact, cot_keyword, cot_reasoning
Adversarial examples: Robustness tests against negative values and anomalies
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:

@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.