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.

翻译：GPTs、Claude、LLaMA和Qwen等模型的涌现重塑了人工智能应用格局，为各行业带来了广阔的新机遇。然而，表格数据的集成能力仍显著不足，尽管其在众多现实领域具有基础性作用。这一空白至关重要，主要基于三个原因：首先，数据库或数据仓库的数据集成对高级应用不可或缺；其次，海量且尚未充分开发的表格数据资源蕴含着巨大的分析潜力；第三，商业智能领域尤其需要当前多数大语言模型难以提供的适应性强且精确的解决方案。为此，我们提出了TableGPT2模型，该模型经过严格预训练，并使用超过59.38万张表格和236万个高质量查询-表格-输出元组进行微调，其表格相关数据规模在先前研究中前所未有。这种大规模训练使TableGPT2在表格中心任务中表现卓越，同时保持强大的通用语言与编码能力。TableGPT2的核心创新之一是其新颖的表格编码器，该编码器专为捕获模式级与单元格级信息而设计，显著增强了模型处理现实应用中常见的模糊查询、缺失列名及不规则表格的能力。类似于视觉语言模型，这种开创性方法与解码器集成，构成了一个鲁棒的大型多模态模型。我们相信实验结果具有说服力：在超过23项基准测试指标中，TableGPT2的7B模型相较于先前基准中立的大语言模型平均性能提升35.20%，72B模型提升49.32%，且其通用能力保持完整。