# Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

arXiv: https://arxiv.org/abs/2005.00969 · HTML: https://mlsanity.com/paper/2005.00969/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 30 of 48)

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- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
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- [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://mlsanity.com/paper/2104.08704/) (2021)
- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors](https://mlsanity.com/paper/2210.06340/) (2022)
- [Transformer-based Conditional Variational Autoencoder for Controllable Story Generation](https://mlsanity.com/paper/2101.00828/) (2021)
- [Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models](https://mlsanity.com/paper/2407.04121/) (2024)
- [Language Models Hallucinate, but May Excel at Fact Verification](https://mlsanity.com/paper/2310.14564/) (2023)
- [Open Domain Question Answering with A Unified Knowledge Interface](https://mlsanity.com/paper/2110.08417/) (2021)
- [Focus Attention: Promoting Faithfulness and Diversity in Summarization](https://mlsanity.com/paper/2105.11921/) (2021)
- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View](https://mlsanity.com/paper/2102.08585/) (2021)
- [Logic-Consistency Text Generation from Semantic Parses](https://mlsanity.com/paper/2108.00577/) (2021)
- [Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search](https://mlsanity.com/paper/2203.08436/) (2022)
- [Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation](https://mlsanity.com/paper/2105.14778/) (2021)
- [Zero-Resource Hallucination Prevention for Large Language Models](https://mlsanity.com/paper/2309.02654/) (2023)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Evaluating Large Language Models for Health-related Queries with Presuppositions](https://mlsanity.com/paper/2312.08800/) (2023)
- [Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations](https://mlsanity.com/paper/2404.03745/) (2024)
- [Stage-wise Fine-tuning for Graph-to-Text Generation](https://mlsanity.com/paper/2105.08021/) (2021)
- [Graphine: A Dataset for Graph-aware Terminology Definition Generation](https://mlsanity.com/paper/2109.04018/) (2021)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events](https://mlsanity.com/paper/2101.00822/) (2021)
- [Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generation](https://mlsanity.com/paper/2304.11791/) (2023)
- [Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies](https://mlsanity.com/paper/2204.05879/) (2022)
- [The Effects of Hallucinations in Synthetic Training Data for Relation Extraction](https://mlsanity.com/paper/2410.08393/) (2024)
- [Poly-FEVER: A Multilingual Fact Verification Benchmark for Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2503.16541/) (2025)

## Abstract (arXiv)

Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table. In this paper, for the first time, we propose a novel Transformer-based generation framework to achieve the goal. The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a table-text embedding similarity loss based on the Transformer model. Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem. We also provide detailed analysis on each component of our model in our experiments. Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.