FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation
arXiv:2602.22273v1 Announce Type: new Abstract: We introduce FIRE, a comprehensive benchmark designed to evaluate both the theoretical financial knowledge of LLMs and their ability to handle practical business scenarios. For theoretical assessment, we curate a diverse set of examination questions drawn from widely recognized financial qualification exams, enabling evaluation of LLMs deep understanding and application of financial knowledge. In addition, to assess the practical value of LLMs in real-world financial tasks, we propose a systematic evaluation matrix that categorizes complex financial domains and ensures coverage of essential subdomains and business activities. Based on this evaluation matrix, we collect 3,000 financial scenario questions, consisting of closed-form decision questions with reference answers and open-ended questions evaluated by predefined rubrics. We conduct comprehensive evaluations of state-of-the-art LLMs on the FIRE benchmark, including XuanYuan 4.0, ou
arXiv:2602.22273v1 Announce Type: new Abstract: We introduce FIRE, a comprehensive benchmark designed to evaluate both the theoretical financial knowledge of LLMs and their ability to handle practical business scenarios. For theoretical assessment, we curate a diverse set of examination questions drawn from widely recognized financial qualification exams, enabling evaluation of LLMs deep understanding and application of financial knowledge. In addition, to assess the practical value of LLMs in real-world financial tasks, we propose a systematic evaluation matrix that categorizes complex financial domains and ensures coverage of essential subdomains and business activities. Based on this evaluation matrix, we collect 3,000 financial scenario questions, consisting of closed-form decision questions with reference answers and open-ended questions evaluated by predefined rubrics. We conduct comprehensive evaluations of state-of-the-art LLMs on the FIRE benchmark, including XuanYuan 4.0, our latest financial-domain model, as a strong in-domain baseline. These results enable a systematic analysis of the capability boundaries of current LLMs in financial applications. We publicly release the benchmark questions and evaluation code to facilitate future research.
Executive Summary
The article introduces FIRE, a comprehensive benchmark designed to evaluate the financial intelligence and reasoning capabilities of large language models (LLMs). FIRE includes both theoretical assessments, drawn from recognized financial qualification exams, and practical evaluations based on real-world financial scenarios. The benchmark consists of 3,000 questions, including closed-form and open-ended questions, evaluated using predefined rubrics. The authors conducted evaluations on state-of-the-art LLMs, including their own financial-domain model, XuanYuan 4.0, to identify capability boundaries in financial applications. The benchmark and evaluation code are publicly released to support future research.
Key Points
- ▸ FIRE benchmark evaluates both theoretical and practical financial knowledge of LLMs.
- ▸ Theoretical assessment includes questions from recognized financial qualification exams.
- ▸ Practical evaluation consists of 3,000 financial scenario questions with closed-form and open-ended formats.
- ▸ State-of-the-art LLMs, including XuanYuan 4.0, were evaluated to identify capability boundaries.
- ▸ Benchmark and evaluation code are publicly released for future research.
Merits
Comprehensive Evaluation
FIRE provides a thorough assessment of LLMs' financial intelligence by covering both theoretical knowledge and practical application, ensuring a holistic evaluation.
Diverse Question Set
The inclusion of 3,000 questions, including closed-form and open-ended formats, ensures a wide range of financial scenarios are evaluated, enhancing the benchmark's robustness.
Public Release
The public release of the benchmark and evaluation code facilitates transparency and encourages further research and development in the field.
Demerits
Potential Bias
The benchmark may be influenced by the selection of questions from recognized financial qualification exams, which could introduce bias towards certain financial domains or regions.
Evaluation Complexity
The evaluation of open-ended questions using predefined rubrics may introduce subjectivity, potentially affecting the consistency and reliability of the results.
Limited Scope
While comprehensive, the benchmark may not cover all possible financial scenarios, leaving some niche or emerging financial domains underrepresented.
Expert Commentary
The introduction of the FIRE benchmark represents a significant advancement in the evaluation of LLMs' financial intelligence and reasoning capabilities. By combining theoretical assessments with practical evaluations, FIRE provides a comprehensive framework that addresses the multifaceted nature of financial applications. The inclusion of a diverse set of questions ensures that the benchmark is robust and covers a wide range of financial scenarios. However, the potential for bias in question selection and the subjectivity in evaluating open-ended questions are notable limitations. The public release of the benchmark and evaluation code is commendable, as it promotes transparency and encourages further research. The implications of this benchmark extend beyond academic research, offering practical insights for financial institutions and policy considerations for regulators. As AI continues to play an increasingly prominent role in the financial sector, benchmarks like FIRE will be instrumental in ensuring that these technologies are used responsibly and effectively.
Recommendations
- ✓ Future iterations of the FIRE benchmark should aim to diversify the question set further to minimize bias and ensure comprehensive coverage of all financial domains.
- ✓ Developing more objective and standardized evaluation methods for open-ended questions could enhance the consistency and reliability of the benchmark's results.
