Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model

• URL: http://arxiv.org/abs/2404.01786v1
• Date: Tue, 2 Apr 2024 09:49:53 GMT
• Title: Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model
• Authors: Rohit Pandey, Hetvi Waghela, Sneha Rakshit, Aparna Rangari, Anjali Singh, Rahul Kumar, Ratnadeep Ghosal, Jaydip Sen,
• Abstract summary: This work delved into the realm of automatic text generation, exploring a variety of techniques ranging from traditional deterministic approaches to more modern 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.
• Score: 2.6320841968362645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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