Papers Table Retrieval
“Table Retrieval” 태그가 달린 논문 45편 · 필터 해제
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 RetrievalTowards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argue that this linearization of tables discards their essential geometr…
Representation LearningTable RetrievalRetrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method
Retrieving relevant tables from extensive databases for a given natural language query is essential for accurately answering questions in tasks such as text-to-SQL. Existing table retrieval approaches select a pre-determ…
Table RetrievalGraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources. Exi…
Semantic SimilarityKnowledge GraphsTable RetrievalFGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning
With the rapid advancement of large language models (LLMs), growing efforts have been made on LLM-based table retrieval. However, existing studies typically focus on single-table query, and implement it by similarity mat…
Table RetrievalFine-Grained Table Retrieval Through the Lens of Complex Queries
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 re…
Open-Domain Question AnsweringTable RetrievalEfficient Table Retrieval and Understanding with Multimodal Large Language Models
Tabular data is frequently captured in image form across a wide range of real-world scenarios such as financial reports, handwritten records, and document scans. These visual representations pose unique challenges for ma…
Answer GenerationTable RetrievalCSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale
Natural language to SQL translation (Text-to-SQL) is one of the long-standing problems that has recently benefited from advances in Large Language Models (LLMs). While most academic Text-to-SQL benchmarks request schema …
Table RetrievalModelTables: A Corpus of Tables about Models
We present ModelTables, a benchmark of tables in Model Lakes that captures the structured semantics of performance and configuration tables often overlooked by text only retrieval. The corpus is built from Hugging Face m…
Information RetrievalSemantic RetrievalTable RetrievalExploring Multi-Table Retrieval Through Iterative Search
Open-domain question answering over datalakes requires retrieving and composing information from multiple tables, a challenging subtask that demands semantic relevance and structural coherence (e.g., joinability). While …
Open-Domain Question AnsweringTable RetrievalTaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their…
Reinforcement LearningTable RetrievalFrom Surface to Semantics: Semantic Structure Parsing for Table-Centric Document Analysis
Documents are core carriers of information and knowl-edge, with broad applications in finance, healthcare, and scientific research. Tables, as the main medium for structured data, encapsulate key information and are amon…
Semantic ParsingTable RetrievalTable DetectionToward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence
Tables are fundamental in domains such as finance, healthcare, and public administration, yet real-world table tasks often involve noise, structural heterogeneity, and semantic complexity--issues underexplored in existin…
Domain GeneralizationDomain AdaptationTable RetrievalOpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering
Open-domain table question answering traditionally relies on a two-stage pipeline: static table retrieval followed by a closed-domain answer. In contrast, we propose an end-to-end agentic framework that embeds multi-turn…
Language ModelingLanguage ModellingLarge Language ModelQuestion Answering+1TARGET: Benchmarking Table Retrieval for Generative Tasks
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 model…
BenchmarkingRepresentation LearningRetrievalRetrieval-augmented Generation+3