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    <abstract>Logical table-to-text generation (LT2T) seeks to produce logically faithful textual descriptions base on tables. Current end-to-end LT2T models, which use descriptions directly as learning objectives, frequently face challenges in maintaining logical faithfulness due to the lack of a reasoning knowledge. Recent research have introduced reasoning knowledge generated by models for LT2T task, but the noise along with it limited its performance. We therefore propose a framework reasoning knowledge filter that leverages the collaboration between large language models and smaller models to filter data points with high-quality reasoning knowledge. This framework aims to provide highly matched table, description and reasoning knowledge triplets for LT2T. The results obtained on LogicNLG database demonstrate that the efficiencies of the method in this paper has achieved optimal performance with a reduced amount of data. Specifically, it enhances SP-Acc by 1.4 points and NLI-Acc by 0.7 points compared to the current state-of-the-art model.</abstract>
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%0 Conference Proceedings
%T Reasoning Knowledge Filter for Logical Table-to-Text Generation
%A Bai, Yu
%A Liu, Baoqiang
%A Xue, Shuang
%A Cai, Fang
%A Ye, Na
%A Zhang, Guiping
%Y Liu, Kang
%Y Song, Yangqiu
%Y Han, Zhen
%Y Sifa, Rafet
%Y He, Shizhu
%Y Long, Yunfei
%S Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025
%D 2025
%8 January
%I ELRA and ICCL
%C Abu Dhabi, UAE
%F bai-etal-2025-reasoning
%X Logical table-to-text generation (LT2T) seeks to produce logically faithful textual descriptions base on tables. Current end-to-end LT2T models, which use descriptions directly as learning objectives, frequently face challenges in maintaining logical faithfulness due to the lack of a reasoning knowledge. Recent research have introduced reasoning knowledge generated by models for LT2T task, but the noise along with it limited its performance. We therefore propose a framework reasoning knowledge filter that leverages the collaboration between large language models and smaller models to filter data points with high-quality reasoning knowledge. This framework aims to provide highly matched table, description and reasoning knowledge triplets for LT2T. The results obtained on LogicNLG database demonstrate that the efficiencies of the method in this paper has achieved optimal performance with a reduced amount of data. Specifically, it enhances SP-Acc by 1.4 points and NLI-Acc by 0.7 points compared to the current state-of-the-art model.
%U https://aclanthology.org/2025.neusymbridge-1.3/
%P 18-30

Markdown (Informal)

[Reasoning Knowledge Filter for Logical Table-to-Text Generation](https://aclanthology.org/2025.neusymbridge-1.3/) (Bai et al., NeusymBridge 2025)

• Reasoning Knowledge Filter for Logical Table-to-Text Generation (Bai et al., NeusymBridge 2025)

ACL

• Yu Bai, Baoqiang Liu, Shuang Xue, Fang Cai, Na Ye, and Guiping Zhang. 2025. Reasoning Knowledge Filter for Logical Table-to-Text Generation. In Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025, pages 18–30, Abu Dhabi, UAE. ELRA and ICCL.