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

arXiv: https://arxiv.org/abs/2404.01786 · HTML: https://mlsanity.com/paper/2404.01786/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach](https://mlsanity.com/paper/2406.05418/) (2024)
- [Microsaccade-Inspired Probing: Positional Encoding Perturbations Reveal LLM Misbehaviours](https://mlsanity.com/paper/2510.01288/) (2025)

## Abstract (arXiv)

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.