Key Points

1. Spreadsheets present unique challenges for large language models (LLMs) due to their extensive grids, varied formatting options, and two-dimensional layouts which are poorly suited to linear input.

2. The S PREAD SHEET LLM framework introduces S HEET C OMPRESSOR, an innovative encoding method comprising structural-anchor-based compression, inverted index translation, and data-format-aware aggregation to optimize LLMs' understanding of spreadsheets.

3. S HEET C OMPRESSOR significantly improves performance in spreadsheet table detection, achieving a 25.6% improvement in GPT4's in-context learning setting and an average compression ratio of 25×.

4. The Chain of Spreadsheet (CoS) methodology, inspired by the Chain of Thought (CoT) methodology, provides a novel approach to decompose spreadsheet reasoning into a table detection-match-reasoning pipeline.

5. Different models and methods were evaluated, and the fine-tuned GPT4 model achieved a new state-of-the-art F1 score in spreadsheet table detection, surpassing previous methods by 12.3%.

6. The CoS method significantly increased model accuracy and reduced redundant data, optimizing the processing efficiency and focus on relevant data.

7. The model fine-tuned for spreadsheet table detection demonstrated robust generalization capabilities across downstream tasks and outperformed established baselines in the Table QA domain.

8. Ablation studies on different modules showed the critical role of structural-anchor-based extraction and the potential for further improvement in processing format details and advanced compression techniques.

9. The S PREAD SHEET LLM framework significantly reduces token usage for spreadsheet encoding, leading to substantial cost reduction in computational load and enhancing the applicability of LLMs for spreadsheet understanding and analysis.

Summary

The paper introduces the SPREADSHEET LLM, a framework designed to optimize large language models' (LLMs) performance on spreadsheet understanding and reasoning. The SPREADSHEET LLM's novel encoding method, called SHEET COMPRESSOR, compresses spreadsheets effectively to address the challenges posed by their extensive two-dimensional grids, flexible layouts, and varied formatting options. The study proposes a vanilla serialization approach that incorporates cell addresses, values, and formats but is limited by LLMs' token constraints. To tackle this, the authors develop SHEET COMPRESSOR, an innovative encoding framework comprising structural-anchor-based compression, inverse index translation, and data-format-aware aggregation, significantly improving spreadsheet table detection performance.

The study demonstrates the effectiveness of the SHEET COMPRESSOR framework by achieving a state-of-the-art 78.9% F1 score in fine-tuned LLM models, surpassing the best existing models by 12.3%. Furthermore, the framework enhances the understanding of spreadsheet layouts and structures and effectively reduces token usage for spreadsheet encoding by 96%. The proposed Chain of Spreadsheet framework is validated for downstream tasks of spreadsheet understanding, showcasing the SPREADSHEET LLM's effectiveness across a variety of spreadsheet tasks.

Performance Improvements in Spreadsheet Table Detection

The study demonstrates notable performance improvements in spreadsheet table detection, surpassing the previous state-of-the-art method by 25.6% in GPT4's in-context learning setting. Additionally, the proposed CoS method for downstream tasks contributes to a significant accuracy improvement over the baseline GPT4 model and other existing baselines. Ablation studies demonstrate the critical role of the extraction and aggregation modules in capturing and retaining key structural information, significantly reducing the number of required tokens for spreadsheet encoding.

Advancements and Future Research of the SPREADSHEET LLM Framework

The SPREADSHEET LLM framework introduces substantial advancements in processing and understanding spreadsheet data, with significant improvements in token usage reduction and computational costs. The proposed framework's future research could further explore spreadsheet format details and advanced semantic compression techniques to enhance its capabilities. The study was conducted transparently, maintaining the privacy and confidentiality of participants and their rights and interests.

Reference: https://arxiv.org/abs/2407.09025