Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios

• URL: http://arxiv.org/abs/2305.14987v2
• Date: Mon, 30 Oct 2023 22:00:25 GMT
• Title: Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios
• Authors: Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, Arman Cohan
• Abstract summary: Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The adoption of large language models (LLMs) in real-world applications for table information seeking remains underexplored. This paper investigates the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios.
• Score: 32.84523661055774
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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