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Computer Science > Computation and Language

arXiv:2404.01786 (cs)
[Submitted on 2 Apr 2024]

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
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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.
Comments: This report pertains to the Capstone Project done by Group 5 of the Fall batch of 2023 students at Praxis Tech School, Kolkata, India. The reports consists of 57 pages and it includes 17 figures and 8 tables. This is the preprint which will be submitted to IEEE CONIT 2024 for review
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2404.01786 [cs.CL]
  (or arXiv:2404.01786v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2404.01786

Submission history

From: Jaydip Sen [view email]
[v1] Tue, 2 Apr 2024 09:49:53 UTC (1,706 KB)
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