ModelTables: A Corpus of Tables about Models

• URL: http://arxiv.org/abs/2512.16106v1
• Date: Thu, 18 Dec 2025 02:51:46 GMT
• Title: ModelTables: A Corpus of Tables about Models
• Authors: Zhengyuan Dong, Victor Zhong, Renée J. Miller,
• Abstract summary: We present ModelTables, a benchmark of tables in Model Lakes that captures structured semantics of performance and configuration.<n>Compared with open data lake tables, model tables are smaller yet exhibit inter table relationships.<n>We present one extensive empirical use case for the benchmark which is table search.
• Score: 7.843042548609869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 model cards, GitHub READMEs, and referenced papers, linking each table to its surrounding model and publication context. Compared with open data lake tables, model tables are smaller yet exhibit denser inter table relationships, reflecting tightly coupled model and benchmark evolution. The current release covers over 60K models and 90K tables. To evaluate model and table relatedness, we construct a multi source ground truth using three complementary signals: (1) paper citation links, (2) explicit model card links and inheritance, and (3) shared training datasets. We present one extensive empirical use case for the benchmark which is table search. We compare canonical Data Lake search operators (unionable, joinable, keyword) and Information Retrieval baselines (dense, sparse, hybrid retrieval) on this benchmark. Union based semantic table retrieval attains 54.8 % P@1 overall (54.6 % on citation, 31.3 % on inheritance, 30.6 % on shared dataset signals); table based dense retrieval reaches 66.5 % P@1, and metadata hybrid retrieval achieves 54.1 %. This evaluation indicates clear room for developing better table search methods. By releasing ModelTables and its creation protocol, we provide the first large scale benchmark of structured data describing AI model. Our use case of table discovery in Model Lakes, provides intuition and evidence for developing more accurate semantic retrieval, structured comparison, and principled organization of structured model knowledge. Source code, data, and other artifacts have been made available at https://github.com/RJMillerLab/ModelTables.

Related papers

• RelBench v2: A Large-Scale Benchmark and Repository for Relational Data [33.795728412518834]
  We introduce RelBench v2, a major expansion of the RelBench benchmark for RDL.<n>RDL models consistently outperform single-table baselines across autocomplete forecasting, and recommendation tasks.
  arXiv  Detail & Related papers  (2026-02-13T04:23:48Z)
• LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence [61.46575527504109]
  LimiX-16M and LimiX-2M treat structured data as a joint distribution over variables and missingness.<n>We evaluate LimiX models across 11 large structured-data benchmarks with broad regimes of sample size, feature dimensionality, class number, categorical-to-numerical feature ratio, missingness, and sample-to-feature ratios.
  arXiv  Detail & Related papers  (2025-09-03T17:39:08Z)
• Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA [51.3033125256716]
  We model the subgraph retrieval task as a conditional generation task handled by small language models. Our base generative subgraph retrieval model, consisting of only 220M parameters, competitive retrieval performance compared to state-of-the-art models. Our largest 3B model, when plugged with an LLM reader, sets new SOTA end-to-end performance on both the WebQSP and CWQ benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T15:22:36Z)
• TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes [30.760442756183505]
  We present TabFM, a neural tabular model for data discovery over data lakes.<n>We finetune the pretrained model for identifying unionable, joinable, and subset table pairs.<n>Our results demonstrate significant improvements in F1 scores for search compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-06-28T17:28:53Z)
• LaTable: Towards Large Tabular Models [63.995130144110156]
  Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
  arXiv  Detail & Related papers  (2024-06-25T16:03:50Z)
• KET-QA: A Dataset for Knowledge Enhanced Table Question Answering [63.56707527868466]
  We propose to use a knowledge base (KB) as the external knowledge source for TableQA. Every question requires the integration of information from both the table and the sub-graph to be answered. We design a retriever-reasoner structured pipeline model to extract pertinent information from the vast knowledge sub-graph.
  arXiv  Detail & Related papers  (2024-05-13T18:26:32Z)
• Relational Deep Learning: Graph Representation Learning on Relational Databases [69.7008152388055]
  We introduce an end-to-end representation approach to learn on data laid out across multiple tables. Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all data input.
  arXiv  Detail & Related papers  (2023-12-07T18:51:41Z)
• Model Joins: Enabling Analytics Over Joins of Absent Big Tables [9.797488793708624]
  This work puts forth a framework, Model Join, addressing these challenges. The framework integrates and joins the per-table models of the absent tables. The approximation stems from the models, but not from the Model Join framework.
  arXiv  Detail & Related papers  (2022-06-21T14:28:24Z)
• Table Retrieval May Not Necessitate Table-specific Model Design [83.27735758203089]
  We focus on the task of table retrieval, and ask: "is table-specific model design necessary for table retrieval?" Based on an analysis on a table-based portion of the Natural Questions dataset (NQ-table), we find that structure plays a negligible role in more than 70% of the cases. We then experiment with three modules to explicitly encode table structures, namely auxiliary row/column embeddings, hard attention masks, and soft relation-based attention biases. None of these yielded significant improvements, suggesting that table-specific model design may not be necessary for table retrieval.
  arXiv  Detail & Related papers  (2022-05-19T20:35:23Z)
• Making Table Understanding Work in Practice [9.352813774921655]
  We discuss three challenges of deploying table understanding models and propose a framework to address them. We present SigmaTyper which encapsulates a hybrid model trained on GitTables and integrates a lightweight human-in-the-loop approach to customize the model.
  arXiv  Detail & Related papers  (2021-09-11T03:38:24Z)
• GitTables: A Large-Scale Corpus of Relational Tables [3.1218214157681277]
  We introduce GitTables, a corpus of 1M relational tables extracted from GitHub. Analyses of GitTables show that its structure, content, and topical coverage differ significantly from existing table corpora. We present three applications of GitTables, demonstrating its value for learned semantic type detection models, completion methods, and benchmarks for table-to-KG matching, data search, and preparation.
  arXiv  Detail & Related papers  (2021-06-14T09:22:09Z)
• AutoRC: Improving BERT Based Relation Classification Models via Architecture Search [50.349407334562045]
  BERT based relation classification (RC) models have achieved significant improvements over the traditional deep learning models. No consensus can be reached on what is the optimal architecture. We design a comprehensive search space for BERT based RC models and employ neural architecture search (NAS) method to automatically discover the design choices.
  arXiv  Detail & Related papers  (2020-09-22T16:55:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.