# PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries

arXiv: https://arxiv.org/abs/2605.18199 · HTML: https://mlsanity.com/paper/2605.18199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 18 of 18)

- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [FeTaQA: Free-form Table Question Answering](https://mlsanity.com/paper/2104.00369/) (2021)
- [Open Question Answering over Tables and Text](https://mlsanity.com/paper/2010.10439/) (2020)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019)
- [Dataset Discovery in Data Lakes](https://mlsanity.com/paper/2011.10427/) (2020)
- [Retrieving Complex Tables with Multi-Granular Graph Representation Learning](https://mlsanity.com/paper/2105.01736/) (2021)
- [StruBERT: Structure-aware BERT for Table Search and Matching](https://mlsanity.com/paper/2203.14278/) (2022)
- [Table Search Using a Deep Contextualized Language Model](https://mlsanity.com/paper/2005.09207/) (2020)
- [Ad Hoc Table Retrieval using Semantic Similarity](https://mlsanity.com/paper/1802.06159/) (2018)
- [TARGET: Benchmarking Table Retrieval for Generative Tasks](https://mlsanity.com/paper/2505.11545/) (2025)
- [Data Lakes: A Survey of Functions and Systems](https://mlsanity.com/paper/2106.09592/) (2021)
- [Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach](https://mlsanity.com/paper/2301.03560/) (2023)
- [AutoDDG: Automated Dataset Description Generation using Large Language Models](https://mlsanity.com/paper/2502.01050/) (2025)
- [Improving Table Retrieval with Question Generation from Partial Tables](https://mlsanity.com/paper/2508.06168/) (2025)
- [Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://mlsanity.com/paper/2510.09671/) (2025)
- [Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models](https://mlsanity.com/paper/2410.04231/) (2024)
- [Leveraging Retrieval Augmented Generative LLMs For Automated Metadata Description Generation to Enhance Data Catalogs](https://mlsanity.com/paper/2503.09003/) (2025)

## Abstract (arXiv)

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 retrieval methods have focused on Table Question Answering, where the goal is to select a single table to answer a question, rather than retrieve and rank relevant datasets. We propose PIPER, a content-driven retrieval method for tabular datasets that uses table profiles and LLM-generated queries embedded for dense retrieval. Designed for dataset search in poor-metadata settings, PIPER outperforms both classical metadata-based baselines and strong TableQA retrieval methods, demonstrating the value of LLM-based content modeling for tabular dataset search.