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