Papers by Junyu Xiong

1 papers
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)

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Challenge: Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs).
Approach: They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively.
Outcome: The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score.

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