Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model
<|reference_start|>Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model: This work delved into the realm of automatic text generation, exploring a variety of techniques ranging from traditional deterministic approaches to more modern stochastic methods. Through analysis of greedy search, beam search, top-k sampling, top-p sampling, contrastive searching, and locally typical searching, this work has provided valuable insights into the strengths, weaknesses, and potential applications of each method. Each text-generating method is evaluated using several standard metrics and a comparative study has been made on the performance of the approaches. Finally, some future directions of research in the field of automatic text generation are also identified.<|reference_end|>
@article{pandey2024generative,
    title={Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2
  Model},
    author={Rohit Pandey, Hetvi Waghela, Sneha Rakshit, Aparna Rangari, Anjali
  Singh, Rahul Kumar, Ratnadeep Ghosal, Jaydip Sen},
    journal={arXiv preprint arXiv:2404.01786},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01786},
    primaryClass={cs.CL}
}