一句话：提出从代码库检索并组合数据预处理流水线的方法，使 LLM 能响应电子表格的自由形式数据清洗请求并持续扩充代码库。
电子表格广泛用于商业与科学场景，但分析前的数据清洗常因拼写错误、格式不一致、缺失值和缺乏标准化而高度临时化，需要专门流水线。本文追问 LLM 能否在电子表格中自如导航，并支持终端用户把自由形式的数据处理请求落地。受 RAG 启发，系统从代码库检索元素来组合数据预处理流水线；新代码与流水线还能保存回代码库，供未来检索，从而持续扩充词汇。综合实验展示了系统质量，以及通过不断复用和积累代码来增强处理能力的效果。
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.
@inproceedings{paperbot8687,
title = {Can an LLM Find Its Way Around a Spreadsheet?},
author = {Cho-Ting Lee and Andrew Neeser and Shengzhe Xu and Jay Katyan and Patrick Cross and Sharanya Pathakota and Marigold Norman and John Simeone and Jaganmohan Chandrasekaran and Naren Ramakrishnan},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00101}
}