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        <title>Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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    <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., Vicuna and LLaMA-2) and GPT-4 models. Our data and code are publicly available at https://github.com/yale-nlp/LLM-T2T.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.17</identifier>
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%0 Conference Proceedings
%T Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios
%A Zhao, Yilun
%A Zhang, Haowei
%A Si, Shengyun
%A Nan, Linyong
%A Tang, Xiangru
%A Cohan, Arman
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhao-etal-2023-investigating
%X 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., Vicuna and LLaMA-2) and GPT-4 models. Our data and code are publicly available at https://github.com/yale-nlp/LLM-T2T.
%R 10.18653/v1/2023.emnlp-industry.17
%U https://aclanthology.org/2023.emnlp-industry.17
%U https://doi.org/10.18653/v1/2023.emnlp-industry.17
%P 160-175

Markdown (Informal)

[Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios](https://aclanthology.org/2023.emnlp-industry.17) (Zhao et al., EMNLP 2023)

• Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios (Zhao et al., EMNLP 2023)

ACL

• Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, and Arman Cohan. 2023. Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 160–175, Singapore. Association for Computational Linguistics.