mlsanityData to 7 Oct 2026

Texygen: A Benchmarking Platform for Text Generation Models

arXiv 1802.01886 ↗First posted 6 Feb 2018cs.CL cs.IR cs.LGcited by 484

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Cited by 42 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the consistency of the generated texts. The Texygen platform could help standardize the research on text generation and facilitate the sharing of fine-tuned open-source implementations among researchers for their work. As a consequence, this would help in improving the reproductivity and reliability of future research work in text generation.

Key facts. Cited by 484 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2018.

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Citing papers per year: 2020: 1; 2021: 52; 2022: 63; 2023: 79; 2024: 95; 2025: 100; 2026: 94.

484 papers.

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  11. 2022DiffusionBERT: Improving Generative Masked Language Models with Diffusion Modelscited by 214
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38 more
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  12. 2023Aligning Language Models with Preferences through f-divergence Minimizationcited by 118
  13. 2023AR-Diffusion: Auto-Regressive Diffusion Model for Text Generationcited by 113
  14. 2023A Critical Evaluation of Evaluations for Long-form Question Answeringcited by 111
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  16. 2022On Decoding Strategies for Neural Text Generatorscited by 89
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  35. 2021XFORMAL: A Benchmark for Multilingual Formality Style Transfercited by 51
  36. 2022PromDA: Prompt-based Data Augmentation for Low-Resource NLU Taskscited by 51
  37. 2025Evaluating the Diversity and Quality of LLM Generated Contentcited by 51
  38. 2024Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scorescited by 50

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