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Datasets:
RusNLPWorld
/
RusFinChain-Eval

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

RusFinChain-Eval — Evaluation Results

This dataset contains evaluation results for 8 open-weight large language models on the RusFinChain benchmark (5,280 symbolic financial reasoning tasks).

📊 Overview

  • Total evaluated records: 8,100
  • Models: 8
  • Domains: 17
  • Topics: 172
  • Levels: 3 (Basic, Intermediate, Advanced)

🤖 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 RusFinChain benchmark. The underlying questions, gold solutions, and reasoning steps are taken from the RusFinChain 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 RusFinChain (MIT License).
  • We do not claim ownership of the model outputs.

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)

Values are aggregated across all 8,100 model responses (mean ± std).

Metric Mean Std
Hard F1 0.6538 0.2956
Fuzzy F1 0.4810 0.1676
Soft F1 0.4594 0.1648
DTW F1 (Bonus) 0.5681 0.2317
DTW F1 (Gate) 0.3895 0.2443
BERTScore 0.6711 0.0667
ROUGE-L 0.2424 0.1845
Final Answer Match 0.2936 0.4554
Final Answer Fuzzy 0.3777 0.4561

📚 Domain Distribution

Domain (RU) Domain (EN) Records
Ценные бумаги Securities 835
Финансовое регулирование Financial Regulation 659
Налоги Taxation 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 Borrowings 475
ESG и устойчивое финансирование ESG and Sustainable Finance 459
Крипто-финансы Crypto Finance 459
Слияния и поглощения (M&A) Mergers & Acquisitions (M&A) 456
Финансовые коэффициенты Financial Ratios 456
Управление рисками Risk Management 448
Амортизация Depreciation 400
Инвестиционные проекты Investment Projects 360
Страхование и актуарные расчёты Insurance and Actuarial Science 272
Корпоративные финансы Corporate Finance 272

📊 Level Distribution

Level Records
Basic 2,610
Intermediate 3,582
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
hard_f1 float Hard F1
fuzzy_f1 float Fuzzy F1
soft_f1 float Soft F1
dtw_f1_bonus float DTW F1 (bonus variant)
dtw_f1_gate float DTW F1 (gate variant)
bertscore float BERTScore
rougeL float ROUGE-L score
final_answer_match int Correct final answer (0/1)
final_answer_fuzzy float Fuzzy final answer score

🚀 Usage 

from datasets import load_dataset

dataset = load_dataset("RusNLPWorld/RusFinChain-Eval", split="train")
print(dataset[0])

Example Record 

{
  "id": "1",
  "level": "Basic",
  "domain": "Страхование и актуарные расчёты",
  "topic": "mortality_prob",
  "model": "phi4-mini:3.8b",
  "question": "Для Егор Дмитриевич Козлов возраст 79 лет, пол женский. Найдите годовую вероятность смерти (в %) по упрощённой актуарной модели.",
  "solution": "Шаг 1: Вероятность смерти = 1.86%",
  "steps": [
    {"step": 1, "description": "Вероятность смерти = a · b^возраст (параметры зависят от пола).", "value": null},
    {"step": 2, "description": "Вероятность смерти = 1.86%", "value": 1.86}
  ],
  "final_answer": 1.86,
  "model_generation": "Шаг 1: Определите возрастную группу и пол. Возрастная группа - 80-89 лет; Пол - Женский. Шаг 2: Примените упрощённую актуарную модель для расчета годовой вероятности смерти (qx) для женщин в возрасте 80-89 лет. Согласно модели, qx = 0.0125 + (возраст - 40) * 0.00075. Шаг 3: Подставьте возраст Егор Дмитриевич Козлова ...",
  "recall": 0.92,
  "precision": 0.88,
  "hard_f1": 0.90,
  "fuzzy_f1": 0.85,
  "soft_f1": 0.87,
  "dtw_f1_bonus": 0.88,
  "dtw_f1_gate": 0.82,
  "bertscore": 0.91,
  "rougeL": 0.84,
  "final_answer_match": 1,
  "final_answer_fuzzy": 0.99
}

Analyzing Results 

import pandas as pd
from datasets import load_dataset

dataset = load_dataset("RusNLPWorld/RusFinChain-Eval", split="train")
df = pd.DataFrame(dataset)

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

# Average Hard F1 by difficulty level
level_f1 = df.groupby('level')['hard_f1'].mean()
print(level_f1)

📄 License

MIT License — applies to evaluation results, metrics, and metadata in this dataset. The underlying benchmark content (RusFinChain) is also released under the MIT License.

📚 Citation

If you use this evaluation dataset, please cite both the original benchmark and the evaluation results:

@misc{rusfinchain2026,
  author = {Arabov, Mullosharaf K.},
  title = {RusFinChain: A Symbolic Financial Reasoning Benchmark for Russian LLMs},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/RusNLPWorld/RusFinChain}
}

@misc{rusfinchaineval2026,
  author = {Arabov, Mullosharaf K.},
  title = {RusFinChain-Eval: Evaluation Results for 8 LLMs on the RusFinChain Benchmark},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/RusNLPWorld/RusFinChain-Eval}
}

👤 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