TableGPT2: A Large Multimodal Model with Tabular Data Integration

• URL: http://arxiv.org/abs/2411.02059v3
• Date: Thu, 07 Nov 2024 03:32:44 GMT
• Title: TableGPT2: A Large Multimodal Model with Tabular Data Integration
• Authors: Aofeng Su, Aowen Wang, Chao Ye, Chen Zhou, Ga Zhang, Gang Chen, Guangcheng Zhu, Haobo Wang, Haokai Xu, Hao Chen, Haoze Li, Haoxuan Lan, Jiaming Tian, Jing Yuan, Junbo Zhao, Junlin Zhou, Kaizhe Shou, Liangyu Zha, Lin Long, Liyao Li, Pengzuo Wu, Qi Zhang, Qingyi Huang, Saisai Yang, Tao Zhang, Wentao Ye, Wufang Zhu, Xiaomeng Hu, Xijun Gu, Xinjie Sun, Xiang Li, Yuhang Yang, Zhiqing Xiao,
• Abstract summary: TableGPT2 is a model rigorously pre-trained and fine-tuned with over 593.8K tables and 2.36M high-quality query-table-outputs. One of TableGPT2's key innovations is its novel table encoder, specifically designed to capture schema-level and cell-level information.
• Score: 22.77225649639725
• License:
• Abstract: The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data remains notably underdeveloped, despite its foundational role in numerous real-world domains. This gap is critical for three main reasons. First, database or data warehouse data integration is essential for advanced applications; second, the vast and largely untapped resource of tabular data offers immense potential for analysis; and third, the business intelligence domain specifically demands adaptable, precise solutions that many current LLMs may struggle to provide. In response, we introduce TableGPT2, a model rigorously pre-trained and fine-tuned with over 593.8K tables and 2.36M high-quality query-table-output tuples, a scale of table-related data unprecedented in prior research. This extensive training enables TableGPT2 to excel in table-centric tasks while maintaining strong general language and coding abilities. One of TableGPT2's key innovations is its novel table encoder, specifically designed to capture schema-level and cell-level information. This encoder strengthens the model's ability to handle ambiguous queries, missing column names, and irregular tables commonly encountered in real-world applications. Similar to visual language models, this pioneering approach integrates with the decoder to form a robust large multimodal model. We believe the results are compelling: over 23 benchmarking metrics, TableGPT2 achieves an average performance improvement of 35.20% in the 7B model and 49.32% in the 72B model over prior benchmark-neutral LLMs, with robust general-purpose capabilities intact.

Related papers

• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning [52.08794743921141]
  We propose Table-LLM-Specialist, or Table-Specialist for short, as a new self-trained fine-tuning paradigm specifically designed for table tasks.
  arXiv  Detail & Related papers  (2024-10-16T02:04:17Z)
• TableRAG: Million-Token Table Understanding with Language Models [53.039560091592215]
  TableRAG is a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding. TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information before providing it to the LMs. Our results demonstrate that TableRAG achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding.
  arXiv  Detail & Related papers  (2024-10-07T04:15:02Z)
• TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning [61.14586098005874]
  Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning. We introduce our Tool-Augmented Reasoning framework for Tables (TART), which integrates LLMs with specialized tools. TART contains three key components: a table formatter to ensure accurate data representation, a tool maker to develop specific computational tools, and an explanation generator to maintain explainability.
  arXiv  Detail & Related papers  (2024-09-18T06:19:59Z)
• Multimodal Table Understanding [26.652797853893233]
  How to directly understand tables using intuitive visual information is a crucial and urgent challenge for developing more practical applications. We propose a new problem, multimodal table understanding, where the model needs to generate correct responses to various table-related requests. We develop Table-LLaVA, a generalist multimodal large language model (MLLM), which significantly outperforms recent open-source MLLM baselines on 23 benchmarks.
  arXiv  Detail & Related papers  (2024-06-12T11:27:03Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• TableLlama: Towards Open Large Generalist Models for Tables [22.56558262472516]
  This paper makes the first step towards developing open-source large language models (LLMs) as generalists for a diversity of table-based tasks. We construct TableInstruct, a new dataset with a variety of realistic tables and tasks, for instruction tuning and evaluating LLMs. We further develop the first open-source generalist model for tables, TableLlama, by fine-tuning Llama 2 (7B) with LongLoRA to address the long context challenge.
  arXiv  Detail & Related papers  (2023-11-15T18:47:52Z)
• HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation [7.69801337810352]
  We conduct parameter-efficient fine-tuning on the LLaMA2 model. Our approach involves injecting reasoning information into the input by emphasizing table-specific row data. On both the FetaQA and QTSumm datasets, our approach achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-15T12:02:52Z)
• OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering [106.73213656603453]
  We develop a simple table-based QA model with minimal annotation effort. We propose an omnivorous pretraining approach that consumes both natural and synthetic data.
  arXiv  Detail & Related papers  (2022-07-08T01:23:45Z)
• 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)

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.