80亿参数模型专攻表格推理，提升数据分析准确性
JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Models
- 构建34类表格任务数据集，融合29个公开数据集与300万张表格
- 通过多步推理生成与强化学习优化，表格分析准确率显著提升
- 适合需要复杂表格分析的科研与业务场景使用
本文提出JT-DA-8B（九天数据分析师8B），一种面向多样化真实场景中复杂表格推理任务的专业大语言模型。为解决表格推理缺乏高质量标注数据的问题，我们整合29个公开表格问答数据集与300万张表格，构建包含34类明确定义任务的综合性训练语料库，并设计自动化流程生成具有真实性的多步分析任务。模型基于开源的80亿参数解码器基础模型JT-Coder-8B进行训练，采用基于LLM评分与工作流对齐的过滤策略，提炼高质量、以表格为中心的数据。训练阶段结合监督微调（SFT）与强化学习（RL）进行优化。后续提出四阶段表格推理工作流：表格预处理、表格感知、工具集成推理与提示工程，以增强模型可解释性与执行准确性。实验结果表明，JT-DA-8B在多种表格推理任务中表现优异，验证了数据驱动生成与工作流驱动优化的有效性。
原文摘要 · Abstract (English)
In this work, we present JT-DA-8B (JiuTian Data Analyst 8B), a specialized large language model designed for complex table reasoning tasks across diverse real-world scenarios. To address the lack of high-quality supervision in tabular reasoning scenarios, we construct a comprehensive and diverse training corpus with 34 well-defined table reasoning tasks, by aggregating 29 public table QA datasets and 3 million tables. An automatic pipeline is proposed to generate realistic multi-step analytical tasks involving reasoning patterns. The model is trained upon open-source JT-Coder-8B model, an 8B-parameter decoder-only foundation model trained from scratch. In the training stage, we leverage LLM-based scoring and workflow-aligned filtering to distill high-quality, table-centric data. Both supervised fine-tuning (SFT) and Reinforcement learning (RL) are adopted to optimize our model. Afterwards, a four-stage table reasoning workflow is proposed, including table preprocessing, table sensing, tool-integrated reasoning, and prompt engineering, to improve model interpretability and execution accuracy. Experimental results show that JT-DA-8B achieves strong performance in various table reasoning tasks, demonstrating the effectiveness of data-centric generation and workflow-driven optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。