Abstract
Structured data, prevalent in tables, databases, and knowledge graphs, poses a significant challenge in its representation. With the advent of large language models (LLMs), there has been a shift towards linearization-based methods, which process structured data as sequential token streams, diverging from approaches that explicitly model structure, often as a graph. Crucially, there remains a gap in our understanding of how these linearization-based methods handle structured data, which is inherently non-linear. This work investigates the linear handling of structured data in encoder-decoder language models, specifically T5. Our findings reveal the model's ability to mimic human-designed processes such as schema linking and syntax prediction, indicating a deep, meaningful learning of structure beyond simple token sequencing. We also uncover insights into the model's internal mechanisms, including the ego-centric nature of structure node encodings and the potential for model compression due to modality fusion redundancy. Overall, this work sheds light on the inner workings of linearization-based methods and could potentially provide guidance for future research.
Abstract (translated)
结构化数据，在表格、数据库和知识图中普遍存在，在表示上带来了显著的挑战。随着大型语言模型的出现（LLMs），线性化方法逐渐成为主流，这些方法将结构化数据处理为序列标记流，从明确建模结构的常见方法（通常是图）中进行了转移。关键是，我们对这些线性化方法如何处理结构化数据的理解仍然存在一定的差距，而这种非线性结构本质上是不可预测的。本文研究了编码器-解码器语言模型（如T5）对结构化数据的线性处理，我们的发现表明，该模型能够模拟人类设计的进程，如模式链接和语法预测，表明在简单的标记序列之外，结构之间有更深的、更富有意义的学习。我们还揭示了模型内部机制的一些洞察，包括结构节点编码的自中心性质和模态融合冗余所带来的模型压缩潜力。总的来说，本文使我们对线性化方法的内部工作原理有了更深入的了解，并为未来的研究提供了指导。
URL
https://arxiv.org/abs/2404.02389
