arXiv:2603.10002v2 Announce Type: replace-cross Abstract: We consider the task of end-to-end spreadsheet generation, where language models produce spreadsheet artifacts to satisfy users' explicit and implicit constraints, specified in natural language. We introduce SpreadsheetArena, a platform for evaluating models' performance on the task via blind pairwise preference votes of LLM-generated spreadsheet workbooks. As with other complex, open-ended tasks, relevant evaluation criteria can vary greatly across use cases, often in ways that are difficult to formalize. Compared to general dialogue o
The proliferation of Large Language Models (LLMs) and their increasing capabilities are pushing the boundaries of autonomous task execution, particularly in complex, structured environments like spreadsheets.
Evaluating LLM performance in generating functional and user-satisfying spreadsheet workbooks is crucial for validating their utility in white-collar automation and understanding user preference decomposition.
The introduction of SpreadsheetArena provides a standardized, preference-based evaluation platform for LLMs tackling end-to-end spreadsheet generation, moving beyond basic code generation to functional artifacts.
- · LLM developers focused on agentic workflows
- · Users seeking automated data management solutions
- · Companies investing in AI-driven productivity tools
- · Manual spreadsheet data entry professionals
- · LLMs lacking robust preference alignment in complex tasks
Improved LLMs will generate more accurate and useful spreadsheet applications from natural language prompts, reducing manual effort.
The ability of LLMs to independently create and manage complex data structures will accelerate the adoption of autonomous agents in financial and operational roles.
As LLMs become adept at formalizing implicit constraints, the definition of 'data analyst' roles may evolve drastically, focusing on higher-level strategy rather than execution.
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Read at arXiv cs.AI