SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
Authors: name: Yuzhang Tian; name: Jianbo Zhao; name: Haoyu Dong; name: Junyu Xiong; name: Shiyu Xia; name: Mengyu Zhou; name: Yun Lin; name: José Cambronero; name: Yeye He; name: Shi Han; name: Dongmei Zhang
Published: 2024-07-12 06:34:21

Abstract: Spreadsheets, with their extensive two-dimensional grids, various layouts,
and diverse formatting options, present notable challenges for large language
models (LLMs). In response, we introduce SpreadsheetLLM, pioneering an
efficient encoding method designed to unleash and optimize LLMs' powerful
understanding and reasoning capability on spreadsheets. Initially, we propose a
vanilla serialization approach that incorporates cell addresses, values, and
formats. However, this approach was limited by LLMs' token constraints, making
it impractical for most applications. To tackle this challenge, we develop
SheetCompressor, an innovative encoding framework that compresses spreadsheets
effectively for LLMs. It comprises three modules: structural-anchor-based
compression, inverse index translation, and data-format-aware aggregation. It
significantly improves performance in spreadsheet table detection task,
outperforming the vanilla approach by 25.6% in GPT4's in-context learning
setting. Moreover, fine-tuned LLM with SheetCompressor has an average
compression ratio of 25 times, but achieves a state-of-the-art 78.9% F1 score,
surpassing the best existing models by 12.3%. Finally, we propose Chain of
Spreadsheet for downstream tasks of spreadsheet understanding and validate in a
new and demanding spreadsheet QA task. We methodically leverage the inherent
layout and structure of spreadsheets, demonstrating that SpreadsheetLLM is
highly effective across a variety of spreadsheet tasks.