A multi-agent LLM framework with Manager, Action, and Reflection agents translates natural language into BNF-constrained spreadsheet commands, claiming about 80 percent success on simple tasks and 70 percent on multi step tasks on an undisclosed self-curated benchmark.
TableTalk: Scaffolding Spreadsheet Development with a Language Agent
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abstract
Spreadsheet programming is challenging. Programmers use spreadsheet programming knowledge (e.g., formulas) and problem-solving skills to combine actions into complex tasks. Advancements in large language models have introduced language agents that observe, plan, and perform tasks, showing promise for spreadsheet creation. We present TableTalk, a spreadsheet programming agent embodying three design principles -- scaffolding, flexibility, and incrementality -- derived from studies with seven spreadsheet programmers and 85 Excel templates. TableTalk guides programmers through structured plans based on professional workflows, generating three potential next steps to adapt plans to programmer needs. It uses pre-defined tools to generate spreadsheet components and incrementally build spreadsheets. In a study with 20 programmers, TableTalk produced higher-quality spreadsheets 2.3 times more likely to be preferred than the baseline. It reduced cognitive load and thinking time by 12.6%. From this, we derive design guidelines for agentic spreadsheet programming tools and discuss implications on spreadsheet programming, end-user programming, AI-assisted programming, and human-agent collaboration.
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SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation
A multi-agent LLM framework with Manager, Action, and Reflection agents translates natural language into BNF-constrained spreadsheet commands, claiming about 80 percent success on simple tasks and 70 percent on multi step tasks on an undisclosed self-curated benchmark.