RusFinChain: A Russian Benchmark for Verifiable Chain-of-Thought Reasoning in Finance with Fuzzy-Aligned Evaluation
arXiv:2607.01388v1 Announce Type: new Abstract: Multi-step symbolic reasoning is essential for robust financial analysis, yet most benchmarks neglect intermediate reasoning steps. FINCHAIN introduced verifiable Chain-of-Thought (CoT) evaluation but is limited to English. FINESSE-Bench includes a Russian block but relies on multiple-choice questions without step-level supervision. We present RusFinChain, the first Russian-language symbolic benchmark for verifiable CoT reasoning in finance. It spans 17 domains, 172 topics, and comprises 5,280 parameterized examples from executable Python templat
The increasing push for AI sovereignty and the recognition of language-specific AI model limitations are driving the development of specialized benchmarks like RusFinChain.
This development signifies a growing trend towards creating domain-specific and language-specific AI infrastructure, reducing reliance on Western-centric models and fostering localized AI capabilities.
The availability of a robust Russian-language benchmark for financial AI reasoning enables the training and evaluation of more accurate and culturally relevant AI models, potentially shifting economic analysis capabilities.
- · Russian financial institutions
- · Russian AI developers
- · Localized AI ecosystems
- · Financial AI research
- · Generic, English-only AI models
- · Developers without local language expertise
Improved accuracy and reliability of AI-driven financial analysis in Russia.
Potential for Russia to develop independent and competitive financial AI services without foreign dependencies.
Increased global fragmentation of AI development and data standards, driven by national and linguistic specificities.
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Read at arXiv cs.CL