TY - JOUR
KW - Abstractive Summarization
KW - Extractive Summarization
KW - Natural Language Processing
KW - News Summarization
KW - Prompt Engineering
KW - Reinforcement Learning From Human Feedback (RLHF)
AU - Sini Raj Pulari
AU - Maramreddy Umadevi
AU - Shriram K. Vasudevan
AB - ChatGPT 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.
IS - In press
M1 - In press
N2 - ChatGPT 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.
PY - 9998
SE - 1
SP - 1
EP - 9
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Improved Fine-Tuned Reinforcement Learning From Human Feedback Using Prompting Methods for News Summarization
UR - https://www.ijimai.org/journal/bibcite/reference/3530
VL - In press
SN - 1989-1660
ER -