The World of Generative AI: Deepfakes and Large Language Models

Alakananda Mitra, Saraju P. Mohanty, Elias Kougianos
2/6/2024
cs.CYcs.CL

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

We live in the era of Generative Artificial Intelligence (GenAI). Deepfakes and Large Language Models (LLMs) are two examples of GenAI. Deepfakes, in particular, pose an alarming threat to society as they are capable of spreading misinformation and changing the truth. LLMs are powerful language models that generate general-purpose language. However due to its generative aspect, it can also be a risk for people if used with ill intentions. The ethical use of these technologies is a big concern. This short article tries to find out the interrelationship between them.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)

89% match2024

Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives

87% match2024

AI Safety in Generative AI Large Language Models: A Survey

87% match2024

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models

87% match2025

Navigating LLM Ethics: Advancements, Challenges, and Future Directions

87% match2024

Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions

86% match2024

Cite this paper

@article{mitra2024world,
  title  = {The World of Generative AI: Deepfakes and Large Language Models},
  author = {Alakananda Mitra and Saraju P. Mohanty and Elias Kougianos},
  year   = {2024},
  eprint = {2402.04373},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.04373v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2402.04373.svg)](https://codeofpaper.com/paper/2402.04373)

Discussion