Table Retrieval
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Benchmarks
Statcan Dialogue Dataset
Most implemented
TabRank: Chain-of-Thought Distillation for Table Re-Rankers
Ad Hoc Table Retrieval using Semantic Similarity
Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL
Simulating Users in Interactive Web Table Retrieval
Papers
TabRank: Chain-of-Thought Distillation for Table Re-Rankers
The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval. Multi-stage retrieval systems rely heavily on rerankers to refine candidate lists produced by efficient …
Information RetrievalNatural QuestionsPassage RetrievalTable RetrievalAlignment-Guided Largest Table Overlap Size Estimation
Fast estimation of the size of the largest overlap between tables enables blocking and query-by-table retrieval in large table repositories. The first and the state-of-the-art estimator Armadillo improves efficiency by e…
Table RetrievalFT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding responses in external knowledge during inference. However, conventiona RAG systems under-perform on structured tabular data, largely…
Table RetrievalFollowTable: A Benchmark for Instruction-Following Table Retrieval
Table Retrieval (TR) has traditionally been formulated as an ad-hoc retrieval problem, where relevance is primarily determined by topical semantic similarity. With the growing adoption of LLM-based agentic systems, acces…
Semantic SimilarityTable RetrievalImproving Robustness of Tabular Retrieval via Representational Stability
Transformer-based table retrieval systems flatten structured tables into token sequences, making retrieval sensitive to the choice of serialization even when table semantics remain unchanged. We show that semantically eq…
Table RetrievalTowards Universal Tabular Embeddings: A Benchmark Across Data Tasks
Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic search and table-based prediction. Despite …
Representation LearningTable Retrieval