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

arXiv: https://arxiv.org/abs/2302.02962 · HTML: https://mlsanity.com/paper/2302.02962/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation](https://mlsanity.com/paper/2212.07981/) (2022)
- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [Logical Natural Language Generation from Open-Domain Tables](https://mlsanity.com/paper/2004.10404/) (2020)
- [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://mlsanity.com/paper/2108.13740/) (2021)
- [Data-to-text Generation with Entity Modeling](https://mlsanity.com/paper/1906.03221/) (2019)
- [Logic2Text: High-Fidelity Natural Language Generation from Logical Forms](https://mlsanity.com/paper/2004.14579/) (2020)
- [Program Enhanced Fact Verification with Verbalization and Graph Attention Network](https://mlsanity.com/paper/2010.03084/) (2020)
- [ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples](https://mlsanity.com/paper/2210.12374/) (2022)
- [HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data](https://mlsanity.com/paper/2204.13243/) (2022)
- [Logic-Consistency Text Generation from Semantic Parses](https://mlsanity.com/paper/2108.00577/) (2021)
- [PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation](https://mlsanity.com/paper/2205.12697/) (2022)
- [Exploring Decomposition for Table-based Fact Verification](https://mlsanity.com/paper/2109.11020/) (2021)
- [R2D2: Robust Data-to-Text with Replacement Detection](https://mlsanity.com/paper/2205.12467/) (2022)
- [Improving Logical-Level Natural Language Generation with Topic-Conditioned Data Augmentation and Logical Form Generation](https://mlsanity.com/paper/2112.06240/) (2021)

## Built on by (most cited first, 8 of 8)

- [SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation](https://mlsanity.com/paper/2406.14991/) (2024)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios](https://mlsanity.com/paper/2305.14987/) (2023)
- [QTSumm: Query-Focused Summarization over Tabular Data](https://mlsanity.com/paper/2305.14303/) (2023)
- [OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs](https://mlsanity.com/paper/2503.11858/) (2025)
- [FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluation](https://mlsanity.com/paper/2510.13598/) (2025)
- [Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models](https://mlsanity.com/paper/2410.12878/) (2024)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)

## Abstract (arXiv)

Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table content; 2) Diversity: how to generate multiple sentences that offer different perspectives on the table. This work proposes LoFT, which utilizes logic forms as fact verifiers and content planners to control LT2T generation. Experimental results on the LogicNLG dataset demonstrate that LoFT is the first model that addresses unfaithfulness and lack of diversity issues simultaneously. Our code is publicly available at https://github.com/Yale-LILY/LoFT.