SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
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.
669488068db712bad712e469
Yuzhang Tian
669488068db712bad712e46a
Jianbo Zhao
669488068db712bad712e46b
Haoyu Dong
669488068db712bad712e46c
Junyu Xiong
669488068db712bad712e46d
Shiyu Xia
669488068db712bad712e46e
Mengyu Zhou
669488068db712bad712e46f
Yun Lin
669488068db712bad712e470
José Cambronero
669488068db712bad712e471
Yeye He
669488068db712bad712e472
Shi Han
669488068db712bad712e473
Dongmei Zhang
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
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.
SpreadsheetLLM introduces SheetCompressor and Chain of Spreadsheet to enhance LLMs' performance on spreadsheet tasks through efficient encoding and understanding.
LLMs SpreadsheetLLM vanilla serialization SheetCompressor structural-anchor-based compression inverse index translation data-format-aware aggregation in-context learning F1 score spreadsheet QA