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.
NLP-SIR: A Natural Language Approach for Spreadsheet Information Retrieval
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abstract
Spreadsheets are a ubiquitous software tool, used for a wide variety of tasks such as financial modelling, statistical analysis and inventory management. Extracting meaningful information from such data can be a difficult task, especially for novice users unfamiliar with the advanced data processing features of many spreadsheet applications. We believe that through the use of Natural Language Processing (NLP) techniques this task can be made considerably easier. This paper introduces NLP-SIR, a Natural language interface for spreadsheet information retrieval. The results of a recent evaluation which compared NLP-SIR with existing Information retrieval tools are also outlined. This evaluation has shown that NLP-SIR is a more effective method of spreadsheet information retrieval.
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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.