让模型在推理时自动适应新增列，提升表格处理的实用性。
Tabular Incremental Inference
- 用大语言模型和预训练适配器引入外部知识，支持动态列扩展。
- 在8个数据集上表现超越现有方法，有效利用新增列信息。
- 适合需要持续更新表格结构的工业场景，如数据监控与分析。
表格数据是基础的数据结构，其列的动态变化源于技术进步、需求演变与数据整合。然而，传统AI模型在固定列的表格上训练后进行推理，难以应对列的动态变化。为此，本文提出新任务Tabular Incremental Inference（TabII），旨在让已训练模型在推理阶段直接融入新列，增强模型在动态表格场景中的实用性。我们基于信息瓶颈理论将该任务建模为优化问题，强调理想方法需最小化数据与表示间的互信息，同时最大化表示与任务标签间的互信息。据此设计了包含大语言模型占位符和预训练TabAdapter的架构，并引入增量样本凝练模块，以压缩增量列带来的任务相关特征。在8个公开数据集上的实验表明，所提方法能有效利用增量属性，达到当前最优性能。
原文摘要 · Abstract (English)
Tabular data is a fundamental form of data structure. The evolution of table analysis tools reflects humanity's continuous progress in data acquisition, management, and processing. The dynamic changes in table columns arise from technological advancements, changing needs, data integration, etc. However, the standard process of training AI models on tables with fixed columns and then performing inference is not suitable for handling dynamically changed tables. Therefore, new methods are needed for efficiently handling such tables in an unsupervised manner. In this paper, we introduce a new task, Tabular Incremental Inference (TabII), which aims to enable trained models to incorporate new columns during the inference stage, enhancing the practicality of AI models in scenarios where tables are dynamically changed. Furthermore, we demonstrate that this new task can be framed as an optimization problem based on the information bottleneck theory, which emphasizes that the key to an ideal tabular incremental inference approach lies in minimizing mutual information between tabular data and representation while maximizing between representation and task labels. Under this guidance, we design a TabII method with Large Language Model placeholders and Pretrained TabAdapter to provide external knowledge and Incremental Sample Condensation blocks to condense the task-relevant information given by incremental column attributes. Experimental results across eight public datasets show that TabII effectively utilizes incremental attributes, achieving state-of-the-art performance.
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