Synthetically-Generated Text

A Synthetically-Generated Text is a generated text that is produced using NLP techniques.

• Context:
  • It can (typically) be created by adapting the parameters of a pre-trained model to generate text that mimics a specific style, domain, or content.
  • It can (often) involve the use of prompting, where specific prompts guide the Large Language Model in generating text that aligns with the desired output.
  • ...
  • It can support Natural Language Processing (NLP), Data Augmentation, Knowledge Distillation, and the creation of synthetic datasets for training and evaluation of Machine Learning Models.
  • It can improve the performance of models in tasks such as Few-Shot Learning by providing additional context through synthetic input-output examples.
  • It can also be employed in Knowledge Distillation to distill the knowledge of a compute-intensive transformer into a more compact model, achieving state-of-the-art performance on benchmarks such as the GLUE Benchmark.
  • The quality of synthetically generated text can be enhanced through Adversarial Training, where the goal is to make the synthetic text indistinguishable from real text to a classifier.
  • ...
• Example(s):
  • Synthetic tweets reporting battlefield updates, which can be generated through adaptation of a model with domain-specific training data.
  • News stories describing armed conflict or violence, generated by prompting a model with manually written headlines.
  • Synthetic input-output examples for Few-Shot Learning, generated by conditioning GPT-3 on a few examples and using it to generate new ones.
  • Synthetically-Generated Contract Text.
  • ...
• Counter-Example(s):
  • Manually written text.
  • Text generated through simple rule-based methods without the use of machine learning.
• See: Artificial Intelligence, Large Language Models, Natural Language Processing, Data Augmentation, Knowledge Distillation.

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References

2023

• (Halterman, 2023) ⇒ Andrew Halterman. (2023). “Synthetically Generated Text for Supervised Text Analysis.” In: arXiv preprint arXiv:2303.16028. [1](https://arxiv.org/abs/2303.16028)
  • NOTE: It highlights a method to enhance the quality of synthetically generated text, emphasizing the balance between the benefits of synthetic text and the tradeoffs involved in its generation. This work provides insights into optimizing synthetic text for supervised text analysis.

2022

• (He et al., 2022) ⇒ Xuanli He, Islam Nassar, Jamie Kiros, Gholamreza Haffari, and Mohammad Norouzi. (2022). “Generate, Annotate, and Learn: NLP with Synthetic Text." Transactions of the Association for Computational Linguistics, 10: 826-842. [2](https://direct.mit.edu/tacl/article/10/2022/826/108799)
  • NOTE: It explores the role of diversity in synthetic text for natural language processing, demonstrating that simple unconditional generation with random seeds can provide sufficient diversity for crafting diverse language models.

2021

• (Srivastava & Singh, 2021) ⇒ Vivek Srivastava, and Mayank Singh. (2021). “Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text.” In: arXiv preprint arXiv:2108.01861. [3](https://arxiv.org/abs/2108.01861)
  • NOTE: It explores the quality evaluation of synthetically generated code-mixed Hinglish text, identifying factors that influence text quality. This research contributes to the development of high-quality code-mixed text generation models, with a focus on low-resource languages.

2021

• (Yim et al., 2021) ⇒ Moonbin Yim, Yoonsik Kim, Han-Cheol Cho, and Sungrae Park. (2021). “Synthtiger: Synthetic Text Image Generator Towards Better Text Recognition Models.” In: International Conference on Document Analysis and Recognition, pp. 109-124. Cham: Springer International Publishing. [4](https://link.springer.com/chapter/10.1007/978-3-030-86337-1_8)
  • NOTE: It discusses the development of Synthtiger, a tool designed to generate synthetic text images to aid in improving text recognition models, providing guidelines for generating high-quality synthetic images for STR model training.

2021

• (Munir et al., 2021) ⇒ Shaoor Munir, Brishna Batool, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar. (2021). “Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models." In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 1811-1822. [5](https://www.aclweb.org/anthology/2021.eacl-main.159/)
  • NOTE: It addresses the challenge of attributing authorship to synthetically generated text by language models, proposing a method for identifying the source language model of a given piece of synthetic text.

2014

• (Jaderberg et al., 2014) ⇒ Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. (2014). “Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition." arXiv preprint arXiv:1406.2227. [6](https://arxiv.org/abs/1406.2227)
  • NOTE: It pioneers the use of synthetic data and artificial neural networks for natural scene text recognition, detailing the generation of large-scale synthetic datasets for training and validating text recognition models.

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