Table understanding (TU) has achieved promising advancements, but it faces
the challenges of the scarcity of manually labeled tables and the presence of
complex table structures.To address these challenges, we propose HGT, a
framework with a heterogeneous graph (HG)-enhanced large language model (LLM)
to tackle few-shot TU tasks.It leverages the LLM by aligning the table
semantics with the LLM's parametric knowledge through soft prompts and
instruction turning and deals with complex tables by a multi-task pre-training
scheme involving three novel multi-granularity self-supervised HG pre-training
objectives.We empirically demonstrate the effectiveness of HGT, showing that it
outperforms the SOTA for few-shot complex TU on several benchmarks.

通过 HGT 框架，利用异构图增强的大型语言模型来处理少样本的表格理解任务，并通过多任务预训练方案以及三个新颖多粒度自监督异构图预训练目标来处理复杂表格，实验证明了 HGT 的有效性，并在多个基准测试中胜过 SOTA。