TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models

• URL: http://arxiv.org/abs/2503.04396v1
• Date: Thu, 06 Mar 2025 12:50:14 GMT
• Title: TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models
• Authors: Xinyi He, Yihao Liu, Mengyu Zhou, Yeye He, Haoyu Dong, Shi Han, Zejian Yuan, Dongmei Zhang,
• Abstract summary: TableLoRA is a module designed to improve LLMs' understanding of table structure during PEFT.<n>It incorporates special tokens for serializing tables with special token encoder and uses 2D LoRA to encode low-rank information on cell positions.
• Score: 57.005158277893194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important. However, directly applying parameter-efficient fine-tuning (PEFT) techniques to tabular tasks presents significant challenges, particularly in terms of better table serialization and the representation of two-dimensional structured information within a one-dimensional sequence. To address this, we propose TableLoRA, a module designed to improve LLMs' understanding of table structure during PEFT. It incorporates special tokens for serializing tables with special token encoder and uses 2D LoRA to encode low-rank information on cell positions. Experiments on four tabular-related datasets demonstrate that TableLoRA consistently outperforms vanilla LoRA and surpasses various table encoding methods tested in control experiments. These findings reveal that TableLoRA, as a table-specific LoRA, enhances the ability of LLMs to process tabular data effectively, especially in low-parameter settings, demonstrating its potential as a robust solution for handling table-related tasks.

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