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Datasets:
RusNLPWorld
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RusFinQABenchmark

Tasks:
Question Answering
Modalities:
Text
Formats:
json
Languages:
Russian
Size:
1K - 10K
ArXiv:
Tags:
russian
finance
llm-evaluation
chain-of-thought
benchmarking
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metadata 
language:
  - ru
license: mit
size_categories:
  - 5K<n<10K
task_categories:
  - question-answering
pretty_name: RusFinQABenchmark
tags:
  - russian
  - finance
  - llm-evaluation
  - chain-of-thought
  - benchmarking

RusFinQABenchmark — Evaluation Results

This dataset contains evaluation results for 8 open-weight large language models on the RuFinQA benchmark.

📊 Overview

  • Total evaluated records: 8,100
  • Models: 8
  • Domains: 17
  • Topics: 172
  • Levels: 3

🤖 Models Evaluated

Model Records
llama3.2:3b 1,100
phi4-mini:3.8b 1,000
qwen2.5:7b-instruct 1,000
mistral:7b-instruct 1,000
deepseek-r1:7b 1,000
gemma3:4b 1,000
llama3.1:8b 1,000
aya-expanse:8b 1,000

📖 Data Sources, Licensing & Legal Notice

Data Origin

This dataset contains model-generated outputs and evaluation metrics produced by running open-weight large language models on the RuFinQA benchmark. The underlying questions and gold solutions are derived from the RuFinQA dataset.

Ownership & Rights

  • The evaluation results, metrics, and model generations are released under the MIT License.
  • The underlying benchmark questions and gold solutions are subject to the original licensing terms of RuFinQA.
  • We do not claim ownership of the model outputs or the original financial texts used in the benchmark.

Notice‑and‑Takedown Policy

We respect intellectual property rights. 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.

📧 Contact for takedown requests: marabov@kpfu.ru
⏱️ Response time: Within 14 business days.

📈 Key Performance Metrics (aggregated)

Metric Mean Std
final_answer_match 0.62 0.37
recall 0.71 0.29
precision 0.68 0.31
bertscore 0.83 0.11
rouge1 0.58 0.22
rougeL 0.54 0.23

📚 Domain Distribution

Domain (RU) Domain (EN) Records
Ценные бумаги Securities 835
Финансовое регулирование Financial Regulation 659
Налоги Taxation / Taxes 555
Аннуитеты и вклады Annuities and Deposits 508
Финансовые рынки Financial Markets 507
Личные финансы Personal Finance 504
Процентные ставки Interest Rates 475
Кредиты и займы Loans and Borrowing 475
ESG и устойчивое финансирование ESG and Sustainable Finance 459
Крипто-финансы Crypto Finance 459
Слияния и поглощения (M&A) Mergers and Acquisitions (M&A) 456
Финансовые коэффициенты Financial Ratios 456
Управление рисками Risk Management 448
Амортизация Depreciation / Amortization 400
Инвестиционные проекты Investment Projects 360
Страхование и актуарные расчёты Insurance and Actuarial Calculations 272
Корпоративные финансы Corporate Finance 272

📊 Level Distribution

Level Records
Intermediate 3,582
Basic 2,610
Advanced 1,908

📝 Data Structure

Each record contains:

Field Type Description
id string Task identifier
level string Basic / Intermediate / Advanced
domain string Financial domain
topic string Specific topic
model string Model name
question string Question (Russian)
solution string Gold solution
steps list Gold reasoning steps
final_answer float Correct answer
model_generation string Raw model output
recall float Hard recall
precision float Hard precision
final_answer_match int Correct final answer (0/1)
fuzzy_* float Fuzzy metrics
soft_* float Soft metrics
dtw_* float DTW metrics
bertscore float BERTScore
rouge* float ROUGE scores

🚀 Usage 

from datasets import load_dataset

dataset = load_dataset("arabovs-ai-lab/RusFinQABenchmark", split="train")
print(dataset[0])

Example Record 

{
  "id": "arith_COMP_0001_2025_roa",
  "level": "Intermediate",
  "domain": "Финансовые коэффициенты",
  "topic": "Рентабельность активов (ROA)",
  "model": "llama3.2:3b",
  "question": "Рассчитай рентабельность активов (ROA) для компании...",
  "solution": "ROA = 44.691 / 633.696 = 0.0705 (7.05%)",
  "steps": [...],
  "final_answer": 0.0705,
  "model_generation": "ROA = 44.69 / 633.70 = 0.0705",
  "recall": 0.92,
  "precision": 0.88,
  "final_answer_match": 1,
  "bertscore": 0.91,
  "rouge1": 0.84
}

Analyzing Results 

import pandas as pd
from datasets import load_dataset

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

# Convert to DataFrame
df = pd.DataFrame(dataset)

# Calculate accuracy per model
model_acc = df.groupby('model')['final_answer_match'].mean().sort_values(ascending=False)
print(model_acc)

# Filter by domain
df_esg = df[df['domain'] == 'ESG и устойчивое финансирование']
print(f"ESG domain accuracy: {df_esg['final_answer_match'].mean():.3f}")

📄 License

MIT License — applies to evaluation results, metrics, and metadata in this dataset. The underlying benchmark content is subject to the original RuFinQA licensing terms.

📚 Citation

If you use this evaluation dataset, please cite the original RuFinQA paper:

@misc{rufinqa2025,
  author = {Arabov, Mullosharaf K.},
  title = {RuFinQA: A Massive Multi-Task Reasoning Benchmark for Russian Financial Report Understanding},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/arabovs-ai-lab/RuFinQA}
}

👤 Author

Mullosharaf K. Arabov
ORCID: 0000-0003-2525-1183
PhD in Physics and Mathematics, Associate Professor
Department of Data Analysis and Programming Technologies
Kazan (Volga Region) Federal University
📧 marabov@kpfu.ru

🔗 Links

Generated automatically from RuFinQA evaluation pipeline.