Can an LLM Find Its Way Around a Spreadsheet?

Spreadsheets are routinely used in business and scientific contexts, and one of the most vexing challenges is performing data cleaning prior to analysis and evaluation. The ad-hoc and arbitrary nature of data cleaning problems, such as typos, inconsistent formatting, missing values, and a lack of st...

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Bibliographic Details
Published in:Proceedings / International Conference on Software Engineering pp. 294 - 306
Main Authors: Lee, Cho-Ting, Neeser, Andrew, Xu, Shengzhe, Katyan, Jay, Cross, Patrick, Pathakota, Sharanya, Norman, Marigold, Simeone, John, Chandrasekaran, Jaganmohan, Ramakrishnan, Naren
Format: Conference Proceeding
Language:English
Published: IEEE 26.04.2025
Subjects:
Business
Cleaning
code generation
Codes
data cleaning
Data preprocessing
end-user programming
Libraries
LLMs
Pipelines
Programming
Software engineering
Standardization
Vocabulary
ISSN:1558-1225
Online Access:Get full text
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Summary:Spreadsheets are routinely used in business and scientific contexts, and one of the most vexing challenges is performing data cleaning prior to analysis and evaluation. The ad-hoc and arbitrary nature of data cleaning problems, such as typos, inconsistent formatting, missing values, and a lack of standardization, often creates the need for highly specialized pipelines. We ask whether an LLM can find its way around a spreadsheet and how to support end-users in taking their free-form data processing requests to fruition. Just like RAG retrieves context to answer users' queries, we demonstrate how we can retrieve elements from a code library to compose data preprocessing pipelines. Through comprehensive experiments, we demonstrate the quality of our system and how it is able to continuously augment its vocabulary by saving new codes and pipelines back to the code library for future retrieval.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00101