Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios
Yilun Zhao,
Haowei Zhang,
Shengyun Si
et al.
Abstract:Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The advancements in large language models (LLMs) have shown enormous potential to improve user efficiency. However, the adoption of LLMs in real-world applications for table information seeking remains underexplored. In this paper, we investigate the table-to-text capabilities of different LLMs using four datasets within two real-world inf… Show more
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