Generating Tables from the Parametric Knowledge of Language Models

AbstractWe explore generating factual tables from the parametric knowledge of large language models (LLMs). While LLMs have demonstrated impressive capabilities in recreating knowledge bases and generating free-form text, their ability to generate structured tabular data has received little attention. To address this gap, we explore the table generation abilities of eight state-of-the-art LLMs, including GPT-4o and Llama3.1-405B, using three prompting methods: full-table, row-by-row, and cell-by-cell. To facilitate evaluation we introduce WikiTabGen, a new benchmark consisting of 119 manually curated Wikipedia tables and their description. Our findings show that table generation remains challenging, with the best performing model (LLaMA3.1-405B) reaching only 25.4% accuracy. We further analyze how properties like table size, popularity, and numerical content impact performance. This study highlights the unique challenges of LLM-based table generation and offers a foundation for future research in this area. All code, data, and prompts are publicly available.