title: Alpha Excel Benchmark
summary: This study presents a novel benchmark for evaluating Large Language Models
  (LLMs) using challenges derived from the Financial Modeling World Cup (FMWC)
  Excel competitions. We introduce a methodology for converting 113 existing FMWC
  challenges into programmatically evaluable JSON formats and use this dataset to
  compare the performance of several leading LLMs. Our findings demonstrate
  significant variations in performance across different challenge categories,
  with models showing specific strengths in pattern recognition tasks but
  struggling with complex numerical reasoning. The benchmark provides a
  standardized framework for assessing LLM capabilities in realistic
  business-oriented tasks rather than abstract academic problems. This research
  contributes to the growing field of AI benchmarking by establishing proficiency
  among the 1.5 billion people who daily use Microsoft Excel as a meaningful
  evaluation metric that bridges the gap between academic AI benchmarks and
  practical business applications.
paper_authors._id: 681c060caed24541cb9012dd
paper_authors.name: David Noever
paper_authors._id: 681c060caed24541cb9012de
paper_authors.name: Forrest McKee
paper_title: Alpha Excel Benchmark
paper_summary: This study presents a novel benchmark for evaluating Large Language Models
  (LLMs) using challenges derived from the Financial Modeling World Cup (FMWC)
  Excel competitions. We introduce a methodology for converting 113 existing FMWC
  challenges into programmatically evaluable JSON formats and use this dataset to
  compare the performance of several leading LLMs. Our findings demonstrate
  significant variations in performance across different challenge categories,
  with models showing specific strengths in pattern recognition tasks but
  struggling with complex numerical reasoning. The benchmark provides a
  standardized framework for assessing LLM capabilities in realistic
  business-oriented tasks rather than abstract academic problems. This research
  contributes to the growing field of AI benchmarking by establishing proficiency
  among the 1.5 billion people who daily use Microsoft Excel as a meaningful
  evaluation metric that bridges the gap between academic AI benchmarks and
  practical business applications.
paper_ai_summary: A novel benchmark for Large Language Models evaluates performance using Financial Modeling World Cup Excel challenges, demonstrating variations in pattern recognition and numerical reasoning.
paper_ai_keywords: Large Language Models Financial Modeling World Cup challenges programmatically evaluable JSON formats pattern recognition numerical reasoning benchmark business-oriented tasks AI benchmarking