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Finally, utilizing an interpretative framework as well as a deductive method this study studied the use of reinforcement learning strategies in the development of natural languages. Although secondary data collection provided a solid foundation, the descriptive method allowed for a complete study. The technical solutions used included the incorporation of RL computer programs, advanced text processing, linguistic analysis, contextual adaptability, and moral concerns. As the consequence of our research, we now have a better understanding of RL-NLG systems, which opens the way to more sophisticated and environmentally conscious text production. This research not only enhances the field of NLG, although it also emphasizes the importance of ethical as well as objective applications for artificial intelligence. The insights have application that encompass intelligent machines to the generation of customized content.

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Natural Language Generation (NLG) with Reinforcement Learning (RL)

  • 会议论文

  • 发表日期：

    2025年

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  • Shakir Khan,
  • Mohammad Serajuddin,
  • Zafarul Hasan,
  • Syed Ahad Murtaza Alvi,
  • Rashid Ayub,
  • Arun Sharma

摘要

Finally, utilizing an interpretative framework as well as a deductive method this study studied the use of reinforcement learning strategies in the development of natural languages. Although secondary data collection provided a solid foundation, the descriptive method allowed for a complete study. The technical solutions used included the incorporation of RL computer programs, advanced text processing, linguistic analysis, contextual adaptability, and moral concerns. As the consequence of our research, we now have a better understanding of RL-NLG systems, which opens the way to more sophisticated and environmentally conscious text production. This research not only enhances the field of NLG, although it also emphasizes the importance of ethical as well as objective applications for artificial intelligence. The insights have application that encompass intelligent machines to the generation of customized content.