Your spreadsheets want to talk 💬
So essentially,
You can talk to your spreadsheets with SHEETCOMPRESSOR
Paper:
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models (20 Pages)
Researchers from Microsoft are interested in effective ways of understanding the content and structure of spreadsheets. They want you to be able to talk to your spreadsheets.
Hmm..What’s the background?
The widespread use of spreadsheets for data management, particularly in platforms such as Microsoft Excel and Google Sheets, highlights the need for effective methods of understanding them. Large Language Models (LLM) face unique challenges when dealing with spreadsheets due to their large grid sizes, two-dimensional layouts, and features like cell addresses and formats.
Prior research has explored various approaches including enhancing Mask-RCNN to leverage visual and spatial information, using LSTMs, and employing pre-trained Language Models for spreadsheet understanding. However, these methods often fall short when dealing with the multi-table nature and large sizes of real-world spreadsheets.
Ok, So what is proposed in the research paper?
The researchers propose SHEETCOMPRESSOR, an innovative encoding framework designed to compress spreadsheets efficiently while retaining crucial layout and structural information. It has 3 components:
Structural Anchors for Efficient Layout Understanding: Since homogeneous rows and columns contribute minimally to layout understanding, this module identifies heterogeneous rows and columns, referred to as "structural anchors," which lie at potential table boundaries
Inverted-Index Translation for Token Efficiency: For better tokenization, this module addresses the token inefficiency of traditional grid-based encoding by employing a lossless inverted-index translation in JSON format
Data Format Aggregation for Numerical Cells: This module leverages the observation that similar numerical cells
The researchers also introduce Chain of Spreadsheet (CoS), a two-stage process comprising Table Identification and Boundary Detection, followed by Response Generation. In the first stage, the compressed spreadsheet and the specific task query are input into the LLM, which identifies the relevant table and its precise boundaries. The second stage involves re-inputting the query and the identified table section into the LLM to generate an accurate response.
What’s next?
The researchers of source identify some limitations of their framework and suggest areas for future work:
Incorporating Spreadsheet Format Details: The current methods do not utilize details like background color and borders because they require a high token count
Advanced Semantic Compression for Natural Language Cells: While SHEETCOMPRESSOR aggregates data regions effectively, it lacks a sophisticated semantic compression method for cells with natural language
So essentially,
You can talk to your spreadsheets with SHEETCOMPRESSOR