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

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📖 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.

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📈 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

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📚 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

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📊 Level Distribution

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

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📝 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

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🚀 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)

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📄 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.

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📚 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}
}

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👤 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

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🔗 Links

• 📊 RusFinChain benchmark: https://huggingface.co/datasets/RusNLPWorld/RusFinChain
• 📊 RusFinChain-Eval results: https://huggingface.co/datasets/RusNLPWorld/RusFinChain-Eval
• 💻 Generator code: [GitHub]
• 📄 Paper: https://arxiv.org/abs/2607.01388