mlsanityData to 7 Oct 2026

AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks

arXiv 2209.00946 ↗First posted 2 Sep 2022cs.DB cs.LGcited by 8

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Abstract (arXiv)

Tabular data analysis is performed every day across various domains. It requires an accurate understanding of field semantics to correctly operate on table fields and find common patterns in daily analysis. In this paper, we introduce the AnaMeta dataset, a collection of 467k tables with derived supervision labels for four types of commonly used field metadata: measure/dimension dichotomy, common field roles, semantic field type, and default aggregation function. We evaluate a wide range of models for inferring metadata as the benchmark. We also propose a multi-encoder framework, called KDF, which improves the metadata understanding capability of tabular models by incorporating distribution and knowledge information. Furthermore, we propose four interfaces for incorporating field metadata into downstream analysis tasks.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

17 of 49 references.

References · 17

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5 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 3.

8 papers.

  1. 2024Large Language Model for Table Processing: A Surveycited by 52
  2. 2023TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoningcited by 32
  3. 2024Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretrainingcited by 12
  4. 2024KET-QA: A Dataset for Knowledge Enhanced Table Question Answeringcited by 5
  5. 2025TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Modelscited by 4
  6. 2024DataTales: A Benchmark for Real-World Intelligent Data Narrationcited by 3
  7. 2025TransactionGPTcited by 0
  8. 2025Seeing isn't Hearing: Benchmarking Vision Language Models at Interpreting Spectrogramscited by 0