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Remember when you had a few hundred rows of data that could easily be opened in Excel. 📊
Remember when you had a few hundred rows of data that could easily be opened in Excel. 📊
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Well, we are far from that with billion-parameter LLMs trained on trillions of tokens. 🌐
Well, we are far from that with billion-parameter LLMs trained on trillions of tokens. 🌐
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@Microsoft
Microsoft
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  wants to bridge that using "SpreadsheetLLM": Encoding Spreadsheets for Large Language Models. 🤖📈
  wants to bridge that using "SpreadsheetLLM": Encoding Spreadsheets for Large Language Models. 🤖📈
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While it sounds simple, Spreadsheets, with their extensive two-dimensional grids, various layouts, and diverse formatting options, present notable challenges for large language models (LLMs). 🚧
While it sounds simple, Spreadsheets, with their extensive two-dimensional grids, various layouts, and diverse formatting options, present notable challenges for large language models (LLMs). 🚧
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They initially propose a vanilla serialization approach that incorporates cell addresses, values, and formats. However, this approach is limited by LLMs' token constraints, making it impractical for most applications. ⛔
They initially propose a vanilla serialization approach that incorporates cell addresses, values, and formats. However, this approach is limited by LLMs' token constraints, making it impractical for most applications. ⛔
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Solution... A SheetCompressor, an innovative encoding framework that compresses spreadsheets effectively for LLMs. 🔧
Solution... A SheetCompressor, an innovative encoding framework that compresses spreadsheets effectively for LLMs. 🔧
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It comprises three modules: 
It comprises three modules: 
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1️⃣ Structural-anchor-based compression
1️⃣ Structural-anchor-based compression
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2️⃣ Inverse index translation
2️⃣ Inverse index translation
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3️⃣ Data-format-aware aggregation
3️⃣ Data-format-aware aggregation
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It significantly improves performance in spreadsheet table detection task, outperforming the vanilla approach by 25.6% in GPT4's in-context learning setting. 🏆
It significantly improves performance in spreadsheet table detection task, outperforming the vanilla approach by 25.6% in GPT4's in-context learning setting. 🏆
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Sounds exciting, sadly no code, models OR datasets are released. 🙁
Sounds exciting, sadly no code, models OR datasets are released. 🙁
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Moreover, there is a lot of research in encoding 2D position embeddings and this work has not been benchmarked against that! 🧐
Moreover, there is a lot of research in encoding 2D position embeddings and this work has not been benchmarked against that! 🧐
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Paper: 
Paper: 
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https://huggingface.co/papers/2407.09025
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2407.09025
https://huggingface.co/papers/2407.09025
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models (2407.09025)
Remember when you had a few hundred rows of data that could easily be opened in Excel. 📊

Well, we are far from that with billion-parameter LLMs trained on trillions of tokens. 🌐

@Microsoft  wants to bridge that using "SpreadsheetLLM": Encoding Spreadsheets for Large Language Models. 🤖📈

While it sounds simple, Spreadsheets, with their extensive two-dimensional grids, various layouts, and diverse formatting options, present notable challenges for large language models (LLMs). 🚧

They initially propose a vanilla serialization approach that incorporates cell addresses, values, and formats. However, this approach is limited by LLMs' token constraints, making it impractical for most applications. ⛔

Solution... A SheetCompressor, an innovative encoding framework that compresses spreadsheets effectively for LLMs. 🔧

It comprises three modules: 
1️⃣ Structural-anchor-based compression
2️⃣ Inverse index translation
3️⃣ 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. 🏆

Sounds exciting, sadly no code, models OR datasets are released. 🙁

Moreover, there is a lot of research in encoding 2D position embeddings and this work has not been benchmarked against that! 🧐

Paper: https://huggingface.co/papers/2407.09025