PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries
📰 ArXiv cs.AI
Learn how PIPER uses LLM-generated pseudoqueries for content-based table search, enabling more effective dataset reuse and analysis
Action Steps
- Build a table profiling system using LLMs to generate rich representations of tables
- Generate pseudoqueries using LLMs to capture table content and context
- Configure a search system to utilize pseudoqueries for content-based table search
- Test the search system using a dataset of tables with varying schema and cell values
- Apply PIPER to a real-world dataset to evaluate its effectiveness in facilitating dataset reuse and analysis
Who Needs to Know This
Data scientists and researchers working with large tabular datasets can benefit from PIPER's content-based search capabilities, improving dataset discovery and reuse
Key Insight
💡 LLM-generated pseudoqueries can effectively capture table content and context, enabling more accurate content-based table search
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🚀 PIPER: Content-Based Table Search via LLM-Generated Pseudoqueries 🚀
Key Takeaways
Learn how PIPER uses LLM-generated pseudoqueries for content-based table search, enabling more effective dataset reuse and analysis
Full Article
Title: PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries
Abstract:
arXiv:2605.18199v1 Announce Type: cross Abstract: The rapid growth of tabular datasets in data lakes, data spaces, and open data portals makes effective dataset search essential for reuse and analysis. Existing search systems rely mainly on metadata, which is often incomplete or low quality, especially for tables whose meaning depends on both schema and cell values. Recent advances in Large Language Models (LLMs) enable richer, content-based representations of tables. However, prior LLM-based re
Abstract:
arXiv:2605.18199v1 Announce Type: cross Abstract: The rapid growth of tabular datasets in data lakes, data spaces, and open data portals makes effective dataset search essential for reuse and analysis. Existing search systems rely mainly on metadata, which is often incomplete or low quality, especially for tables whose meaning depends on both schema and cell values. Recent advances in Large Language Models (LLMs) enable richer, content-based representations of tables. However, prior LLM-based re
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