  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 information seeking
scenarios. These include the LogicNLG and our newly-constructed LoTNLG datasets
for data insight generation, along with the FeTaQA and our newly-constructed
F2WTQ datasets for query-based generation. We structure our investigation
around three research questions, evaluating the performance of LLMs in
table-to-text generation, automated evaluation, and feedback generation,
respectively. Experimental results indicate that the current high-performing
LLM, specifically GPT-4, can effectively serve as a table-to-text generator,
evaluator, and feedback generator, facilitating users' information seeking
purposes in real-world scenarios. However, a significant performance gap still
exists between other open-sourced LLMs (e.g., Tulu and LLaMA-2) and GPT-4
models. Our data and code are publicly available at
https://github.com/yale-nlp/LLM-T2T.
