Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning

arXiv:2406.13846v1 Announce Type: new
Abstract: Recent research has explored how Language Models (LMs) can be used for feature representation and prediction in tabular machine learning tasks. This involves employing text serialization and supervised fine-tuning (SFT) techniques. Despite the simplicity of these techniques, significant gaps remain in our understanding of the applicability and reliability of LMs in this context. Our study assesses how emerging LM technologies compare with traditional paradigms in tabular machine learning and evaluates the feasibility of adopting similar …

abstract arxiv cs.cl cs.lg feature fine-tuning language language models lms machine machine learning prediction relationship representation research serialization sft simplicity supervised fine-tuning tabular tasks text tuning type