Citing the HinPLMs Model: A Step-by-Step Guide

September 13, 2024

If you are using the HinPLMs (Pre-trained Language Models for Hindi) in your research or projects, it is essential to properly cite the original work to give credit to the authors and the contributions they’ve made to the field. This blog will guide you through the citation process while ensuring you understand the significance of this model.

Understanding HinPLMs

HinPLMs are specialized models designed to process and understand Hindi language texts more effectively. As these pre-trained models enhance natural language processing capabilities for Hindi, they serve as invaluable tools for researchers and developers working within this linguistic domain.

How to Cite the HinPLMs Model

Citing the HinPLMs model is straightforward. Here’s how you can do it:

Identify the correct citation format based on the guidelines you are following, such as APA, MLA, or IEEE.
Use the provided bibliographic information to create your citation.

Here’s the citation you should use:

@InProceedings{hinplms2021,
  author = {Huang, Xixuan and Lin, Nankai and Li, Kexin and Wang, Lianxi and Gan, SuiFu},
  title = {HinPLMs: Pre-trained Language Models for Hindi},
  booktitle = {The International Conference on Asian Language Processing},
  year = {2021},
  publisher = {IEEE Xplore}
}

Why Cite This Work?

Just like a delicious recipe that needs the right ingredients to taste great, research requires proper citations to maintain authenticity and credibility. When you cite the HinPLMs, you’re acknowledging the contribution of the authors and supporting the tradition of scholarly communication.

Troubleshooting Citation Issues

In your journey of citation, you might encounter some bumps along the way. Here are some troubleshooting tips:

Incorrect format: Double-check the formatting guidelines for your specific discipline or publication.
Missing information: Ensure that you have included every necessary detail from the citation guidelines.
Searching for publication: If you cannot find the conference paper, try searching on IEEE Xplore or similar databases.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By properly citing the HinPLMs model, you contribute to the integrity of academic work. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.