# TARGET: Benchmarking Table Retrieval for Generative Tasks

arXiv: https://arxiv.org/abs/2505.11545 · HTML: https://mlsanity.com/paper/2505.11545/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 29 of 29)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [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)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [Open Question Answering over Tables and Text](https://mlsanity.com/paper/2010.10439/) (2020)
- [Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach](https://mlsanity.com/paper/2407.16833/) (2024)
- [Jasper and Stella: distillation of SOTA embedding models](https://mlsanity.com/paper/2412.19048/) (2024)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Open Domain Question Answering over Tables via Dense Retrieval](https://mlsanity.com/paper/2103.12011/) (2021)
- [Generative Table Pre-training Empowers Models for Tabular Prediction](https://mlsanity.com/paper/2305.09696/) (2023)
- [In Defense of RAG in the Era of Long-Context Language Models](https://mlsanity.com/paper/2409.01666/) (2024)
- [Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs](https://mlsanity.com/paper/2310.10358/) (2023)
- [GitTables: A Large-Scale Corpus of Relational Tables](https://mlsanity.com/paper/2106.07258/) (2021)
- [TableRAG: Million-Token Table Understanding with Language Models](https://mlsanity.com/paper/2410.04739/) (2024)
- [StructLM: Towards Building Generalist Models for Structured Knowledge Grounding](https://mlsanity.com/paper/2402.16671/) (2024)
- [Auctus: A Dataset Search Engine for Data Augmentation](https://mlsanity.com/paper/2102.05716/) (2021)
- [Multi-Row, Multi-Span Distant Supervision For Table+Text Question](https://mlsanity.com/paper/2112.07337/) (2021)
- [Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach](https://mlsanity.com/paper/2301.03560/) (2023)

## Built on by (most cited first, 17 of 17)

- [LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Science](https://mlsanity.com/paper/2510.01285/) (2025)
- [Granite Embedding R2 Models](https://mlsanity.com/paper/2508.21085/) (2025)
- [Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks](https://mlsanity.com/paper/2604.21696/) (2026)
- [Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis](https://mlsanity.com/paper/2511.04584/) (2025)
- [Research Challenges in Relational Database Management Systems for LLM Queries](https://mlsanity.com/paper/2508.20912/) (2025)
- [Bridging Business Intent and Data: A Benchmark for Automatic Relational Data Product Generation](https://mlsanity.com/paper/2512.15798/) (2025)
- [Fine-Grained Table Retrieval Through the Lens of Complex Queries](https://mlsanity.com/paper/2603.07146/) (2026)
- [TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings](https://mlsanity.com/paper/2607.24130/) (2026)
- [Towards Contextual Sensitive Data Detection](https://mlsanity.com/paper/2512.04120/) (2025)
- [Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval](https://mlsanity.com/paper/2604.12133/) (2026)
- [FollowTable: A Benchmark for Instruction-Following Table Retrieval](https://mlsanity.com/paper/2605.00400/) (2026)
- [PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries](https://mlsanity.com/paper/2605.18199/) (2026)
- [Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents](https://mlsanity.com/paper/2605.24366/) (2026)
- [PROTOCOL: Late Interaction Retrieval for Protein Homolog Search](https://mlsanity.com/paper/2605.29158/) (2026)
- [Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG](https://mlsanity.com/paper/2607.17742/) (2026)
- [Polaris: Learning to Generate Table Descriptions from Retrieval Feedback](https://mlsanity.com/paper/2608.17171/) (2026)
- [Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case](https://mlsanity.com/paper/2608.26750/) (2026)

## Abstract (arXiv)

The data landscape is rich with structured data, often of high value to organizations, driving important applications in data analysis and machine learning. Recent progress in representation learning and generative models for such data has led to the development of natural language interfaces to structured data, including those leveraging text-to-SQL. Contextualizing interactions, either through conversational interfaces or agentic components, in structured data through retrieval-augmented generation can provide substantial benefits in the form of freshness, accuracy, and comprehensiveness of answers. The key question is: how do we retrieve the right table(s) for the analytical query or task at hand? To this end, we introduce TARGET: a benchmark for evaluating TAble Retrieval for GEnerative Tasks. With TARGET we analyze the retrieval performance of different retrievers in isolation, as well as their impact on downstream tasks. We find that dense embedding-based retrievers far outperform a BM25 baseline which is less effective than it is for retrieval over unstructured text. We also surface the sensitivity of retrievers across various metadata (e.g., missing table titles), and demonstrate a stark variation of retrieval performance across datasets and tasks. TARGET is available at https://target-benchmark.github.io.