mlsanityData to 7 Oct 2026

Neural Text Generation from Structured Data with Application to the Biography Domain

arXiv 1603.07771 ↗First posted 24 Mar 2016cs.CLcited by 20

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Abstract (arXiv)

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. We experiment with a new dataset of biographies from Wikipedia that is an order of magnitude larger than existing resources with over 700k samples. The dataset is also vastly more diverse with a 400k vocabulary, compared to a few hundred words for Weathergov or Robocup. Our model builds upon recent work on conditional neural language model for text generation. To deal with the large vocabulary, we extend these models to mix a fixed vocabulary with copy actions that transfer sample-specific words from the input database to the generated output sentence. Our neural model significantly out-performs a classical Kneser-Ney language model adapted to this task by nearly 15 BLEU.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2016.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 6; 2024: 7; 2025: 2; 2026: 1.

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  1. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  2. 2021Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depthcited by 411
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  4. 2023Physics of Language Models: Part 3.2, Knowledge Manipulationcited by 142
  5. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 115
  6. 2023LitCab: Lightweight Language Model Calibration over Short- and Long-form Responsescited by 44
  7. 2024OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMscited by 28
  8. 2024Exploring Precision and Recall to assess the quality and diversity of LLMscited by 27
  9. 2024Data Contamination Report from the 2024 CONDA Shared Taskcited by 19
  10. 2025Unveiling the Potential of Diffusion Large Language Model in Controllable Generationcited by 14
  11. 2023Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Datacited by 13
  12. 2023Queer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Modelscited by 13
8 more
  1. 2024OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMscited by 13
  2. 2022Selection Bias Induced Spurious Correlations in Large Language Modelscited by 9
  3. 2021Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialoguecited by 6
  4. 2022KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLPcited by 1
  5. 2024Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Modelscited by 1
  6. 2025SCE: Scalable Consistency Ensembles Make Blackbox Large Language Model Generation More Reliablecited by 1
  7. 2024F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methodscited by 0
  8. 2026LatticeBridge: Rare-Event Sequential Inference for Faithful Structured Sequence Synthesiscited by 0