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Logical Table-to-Text (LT2T) generation is tasked with generating logicallyfaithful sentences from tables. There currently exists two challenges in thefield: 1) Faithfulness: how to generate sentences that are factually correctgiven the table content; 2) Diversity: how to generate multiple sentences thatoffer different perspectives on the table. This work proposes LoFT, whichutilizes logic forms as fact verifiers and content planners to control LT2Tgeneration. Experimental results on the LogicNLG dataset demonstrate that LoFTis the first model that addresses unfaithfulness and lack of diversity issuessimultaneously. Our code is publicly available athttps://github.com/Yale-LILY/LoFT.

LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control