mlsanityData to 7 Oct 2026

TextBox 2.0: A Text Generation Library with Pre-trained Language Models

arXiv 2212.13005 ↗First posted 26 Dec 2022cs.CLcited by 3

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Abstract (arXiv)

To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers 13 common text generation tasks and their corresponding 83 datasets and further incorporates 45 PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs. We also implement 4 efficient training strategies and provide 4 generation objectives for pre-training new PLMs from scratch. To be unified, we design the interfaces to support the entire research pipeline (from data loading to training and evaluation), ensuring that each step can be fulfilled in a unified way. Despite the rich functionality, it is easy to use our library, either through the friendly Python API or command line. To validate the effectiveness of our library, we conduct extensive experiments and exemplify four types of research scenarios. The project is released at the link: https://github.com/RUCAIBox/TextBox.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 47 of its 67 references that are in the map. First posted on arXiv 26 Dec 2022.

Builds on

47 of 67 references.

References · 47

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35 more
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023Learning to Imagine: Visually-Augmented Natural Language Generationcited by 7
  2. 2023Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluationcited by 7
  3. 2024TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copycited by 0