SOTAVerified

Table Retrieval

When given a query, the goal of this task is to retrieve a relevant table from a (potentially large) collection of tables. The query could be a single sentence (such as a question), or it could also be a conversation. As for the retrieval, the tables could be in the raw form (i.e. the values of each cells), the metadata (such as the title, description), or summary statistics.

Papers

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Showing 1–25 of 27 papers

TitleStatusHype
Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End SystemCode1
Table Retrieval May Not Necessitate Table-specific Model DesignCode1
StruBERT: Structure-aware BERT for Table Search and MatchingCode1
CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question AnsweringCode1
Semantic Table Retrieval using Keyword and Table QueriesCode1
WTR: A Test Collection for Web Table RetrievalCode1
Retrieving Complex Tables with Multi-Granular Graph Representation LearningCode1
Table Search Using a Deep Contextualized Language ModelCode1
OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question AnsweringCode0
TARGET: Benchmarking Table Retrieval for Generative Tasks—0
Bridging Queries and Tables through Entities in Table Retrieval—0
Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective—0
Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval—0
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQLCode0
Simulating Users in Interactive Web Table RetrievalCode0
Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense RetrievalCode0
The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine IntentsCode0
cTBLS: Augmenting Large Language Models with Conversational TablesCode0
Bridge the Gap between Language models and Tabular Understanding—0
End-to-End Table Question Answering via Retrieval-Augmented Generation—0
Table Retrieval Does Not Necessitate Table-specific Model Design—0
CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering—0
Leveraging Schema Labels to Enhance Dataset Search—0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Question Answering via Web Extracted Tables and Pipelined Models—0
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