Enhancing Tabular Intelligence: Leveraging Large Language Models for Predictive Data Science Tasks

This research explores the potential of Large Language Models (LLMs) in comprehending and leveraging the relational and semantic richness of tabular data through large-scale, table-specific pretraining. The proposed approach aims to mitigate the limitations of LLMs in dealing with structured tabular data by compiling a comprehensive corpus of tables and executing large-scale training of Llama-2 on this enriched dataset.