# Fine-Grained Table Retrieval Through the Lens of Complex Queries

arXiv: https://arxiv.org/abs/2603.07146 · HTML: https://mlsanity.com/paper/2603.07146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 7 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [Jasper and Stella: distillation of SOTA embedding models](https://mlsanity.com/paper/2412.19048/) (2024)
- [On the Theoretical Limitations of Embedding-Based Retrieval](https://mlsanity.com/paper/2508.21038/) (2025)
- [The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models](https://mlsanity.com/paper/2408.07702/) (2024)
- [BEAVER: An Enterprise Benchmark for Text-to-SQL](https://mlsanity.com/paper/2409.02038/) (2024)
- [Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL](https://mlsanity.com/paper/2501.12372/) (2025)
- [LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL](https://mlsanity.com/paper/2503.18596/) (2025)
- [Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval](https://mlsanity.com/paper/2404.09889/) (2024)
- [Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System](https://mlsanity.com/paper/2504.09207/) (2025)
- [TARGET: Benchmarking Table Retrieval for Generative Tasks](https://mlsanity.com/paper/2505.11545/) (2025)
- [EnrichIndex: Using LLMs to Enrich Retrieval Indices Offline](https://mlsanity.com/paper/2504.03598/) (2025)
- [Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queries](https://mlsanity.com/paper/2402.08349/) (2024)
- [FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark](https://mlsanity.com/paper/2409.19014/) (2024)
- [MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL](https://mlsanity.com/paper/2402.10666/) (2024)
- [Improving Table Retrieval with Question Generation from Partial Tables](https://mlsanity.com/paper/2508.06168/) (2025)
- [GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise](https://mlsanity.com/paper/2503.21602/) (2025)
- [RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQL](https://mlsanity.com/paper/2507.23104/) (2025)
- [Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL](https://mlsanity.com/paper/2510.14296/) (2025)
- [Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval](https://mlsanity.com/paper/2504.09824/) (2025)
- [Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis](https://mlsanity.com/paper/2511.04584/) (2025)
- [Exploring Multi-Table Retrieval Through Iterative Search](https://mlsanity.com/paper/2511.13418/) (2025)

## Built on by (most cited first, 1 of 1)

- [Toward Multi-Database Query Reasoning for Text2Cypher](https://mlsanity.com/paper/2605.10373/) (2026)

## Abstract (arXiv)

Enabling question answering over tables and databases in natural language has become a key capability in the democratization of insights from tabular data sources. These systems first require retrieval of data that is relevant to a given natural language query, for which several methods have been introduced. In this work we present and study a table retrieval mechanism devising fine-grained typed query decomposition and global connectivity-awareness (DCTR), to handle the challenges induced by open-domain question answering over relational databases in complex usage contexts. We evaluate the effectiveness of the two mechanisms through the lens of retrieval complexity which we measure along the axes of query- and data complexity. Our analyses over industry-aligned benchmarks illustrate the robustness of DCTR for highly composite queries and densely connected databases.