mlsanityData to 7 Oct 2026

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

arXiv 2302.02962 ↗First posted 6 Feb 2023cs.CLcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 31 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

24 of 31 references.

References · 24

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  2. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
  3. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,068
  4. 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
  5. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 484
  6. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  7. 2020ToTTo: A Controlled Table-To-Text Generation Datasetcited by 267
  8. 2020HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Datacited by 263
  9. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  10. 2017Challenges in Data-to-Document Generationcited by 226
  11. 2022Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluationcited by 141
  12. 2022MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Datacited by 120
12 more
  1. 2020Logical Natural Language Generation from Open-Domain Tablescited by 90
  2. 2021Plan-then-Generate: Controlled Data-to-Text Generation via Planningcited by 52
  3. 2019Data-to-text Generation with Entity Modelingcited by 45
  4. 2020Logic2Text: High-Fidelity Natural Language Generation from Logical Formscited by 45
  5. 2020Program Enhanced Fact Verification with Verbalization and Graph Attention Networkcited by 29
  6. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  7. 2022HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Datacited by 28
  8. 2021Logic-Consistency Text Generation from Semantic Parsescited by 23
  9. 2022PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generationcited by 16
  10. 2021Exploring Decomposition for Table-based Fact Verificationcited by 11
  11. 2022R2D2: Robust Data-to-Text with Replacement Detectioncited by 8
  12. 2021Improving Logical-Level Natural Language Generation with Topic-Conditioned Data Augmentation and Logical Form Generationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 3; 2026: 1.

8 papers.

  1. 2024SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulationcited by 100
  2. 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluationcited by 41
  3. 2023Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarioscited by 12
  4. 2023QTSumm: Query-Focused Summarization over Tabular Datacited by 4
  5. 2025OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMscited by 4
  6. 2024Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Modelscited by 1
  7. 2025FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluationcited by 1
  8. 2026Structuring the Space of Perspectivescited by 1