02077nas a2200253   4500000000100000000000100001008004100002260001200043653003000055653002900085653003200114653002300146653002300169653005400192100002000246700002300266700002500289245011400314856005800428300000800486490001300494520130200507022001401809       9998                            d  c02/202510aAbstractive Summarization10aExtractive Summarization10aNatural Language Processing10aNews Summarization10aPrompt Engineering10aReinforcement Learning From Human Feedback (RLHF)1 aSini Raj Pulari1 aMaramreddy Umadevi1 aShriram K. Vasudevan00aImproved Fine-Tuned Reinforcement Learning From Human Feedback Using Prompting Methods for News Summarization  uhttps://www.ijimai.org/journal/bibcite/reference/3530  a1-90 vIn press3 aChatGPT uses a generative pretrained transformer neural network model, which is under the larger umbrella of generative models. One major boom after ChatGPT is the advent of prompt engineering, which is the most critical part of ChatGPT that utilizes Large Language Models (LLM) and helps ChatGPT provide the desired outputs based on the style and tone of interactions carried out with it. Reinforcement learning from human feedback (RLHF) was used as the major aspect for fine-tuning LLM-based models. This work proposes a human selection strategy that is incorporated in the RLHF process to prevent undesirable consequences of the rightful choice of human reviewers for feedback. H-Rouge is a new metric proposed for humanized AI systems. A detailed evaluation of State-of-the-art summarization algorithms and prompt-based methods have been provided as part of the article. The proposed methods have introduced a strategy for human selection of RLHF models which employs multi-objective optimization to balance various goals encountered during the process with H-Rouge. This article will help nuance readers conduct research in the field of text summarization to start with prompt engineering in the summarization field, and future work will help them proceed in the right direction of research.  a1989-1660