🏦 RusFinChain

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

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📊 Overview

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

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📖 Source Material & Data Licensing

Foundational Source
The methodological framework, financial formulas, problem typology, and a significant portion of the practical examples used in this dataset are based on the author's educational manual:

Арабов М.К., Маматкулов А.А., Солиева Л.Ф. Решение финансово-экономических задач посредством Excel: Учебно-методическое пособие / М.К. Арабов, А.А. Маматкулов, Л.Ф. Солиева. – Душанбе: РТСУ, 2019. – 200 с.

This dataset significantly extends the book's materials by generating large‑scale Chain‑of‑Thought (CoT) reasoning traces, adding diverse question types (adversarial, multi‑step, etc.), and structuring them into a unified benchmark for LLM evaluation.

Data Provenance & Rights
The benchmark includes anonymized excerpts and synthetic financial indicators inspired by real‑world reporting standards of Russian companies. All proprietary identifiers (TINs, company names, specific monetary values tied to real entities) have been fully pseudonymized or replaced with synthetic analogs.

• Annotations, questions, reasoning chains, and metadata are distributed under the MIT License.
• Underlying textual patterns and raw financial data structures are used strictly for academic research. We do not claim ownership of the original financial reports of specific companies.

Notice‑and‑Takedown Policy
We follow the industry‑standard practice (similar to HPLT and other large‑scale corpora): the packaging and annotations are openly licensed, but the rights to the original source texts belong to their respective holders.

If you are a copyright owner and believe that your content appears in this dataset without proper authorization, please contact us. We will promptly remove the disputed entries upon verification.

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🧩 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

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📈 Difficulty Distribution

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

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🔗 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%)"
}

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📝 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

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🚀 Usage

Loading the Benchmark

from datasets import load_dataset

dataset = load_dataset("arabovs-ai-lab/RusFinChain", 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/RusFinChain", 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}")

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🧠 Evaluated Models

Model evaluations will be added in future releases.

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⚙️ 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

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📚 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

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📄 License

MIT (for annotations and metadata; underlying source texts are subject to original rights – see Source Material & Data Licensing above).

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📖 Citation

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

@misc{rusfinchain2026,
  author = {Arabov, Mullosharaf K.},
  title = {RusFinChain: 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/RusFinChain}},
}

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👤 Author

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

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🔗 Links

• 📊 Dataset: https://huggingface.co/datasets/arabovs-ai-lab/RusFinChain
• 💻 Generator code: [GitHub]
• 📄 Paper: https://arxiv.org/abs/2607.01388

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Automatically generated from the RusFinChain evaluation pipeline.