arXiv:2605.30907v1 Announce Type: cross Abstract: We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet workbooks in the professional finance domain. Though estimates of the global population of paying users of spreadsheet software range in the hundreds of millions -- an order of magnitude more than the estimated global population of professional developers -- comparatively fewer resources have been devoted to exploring and expanding LLM capabilities in the spreadsheet domain, with fewer still dedicat
The rapid advancement of large language models and the increasing demand for their application in specific domain tasks, combined with the ubiquitous use of spreadsheets in finance, makes this benchmark timely.
It highlights a critical bottleneck and opportunity for LLMs to automate a significant portion of white-collar work previously considered outside their immediate grasp, impacting productivity and job roles in finance.
The development of robust benchmarks for LLM agents in complex, high-stakes financial spreadsheet tasks indicates a crucial step towards their practical deployment in professional settings, moving beyond general text generation.
- · LLM developers (e.g., OpenAI, Google)
- · Financial institutions adopting AI for efficiency
- · Productivity software companies integrating AI agents
- · Data scientists and AI researchers
- · Entry-level financial data analysts
- · Traditional business process outsourcing firms
- · Consulting firms performing repetitive data tasks
Increased efficiency and reduced human error in financial data analysis and reporting through LLM agent deployment.
Shifting demand for human skills in finance from manual data manipulation to higher-level strategic analysis and AI supervision.
Potential for new financial instruments and market strategies enabled by hyper-efficient, AI-driven data processing and scenario modeling.
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