A Review of Current Concerns and Mitigation Strategies on Generative AI and LLMs
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A Review of Current Concerns and Mitigation Strategies on Generative AI and LLMs
Ruchika1, Hemant Singh2, Astitva Singh3, Himanshu Bansal4, Sanna Mehraj Kak5*
1,2,3,4,5 Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
ruchika@niet.co.in, hemant31400@gmail.com, astitvasingh4122@gmail.com, 0211csai061@niet.co.in, sanah.mehraj@gmail.com
Abstract
The upcoming of the large language models and generative artificial intelligence had Completely change the way in which we generate and understand language, and also start the beginning of a new phase in AI-driven applications. This review paper over see the advancements and changes that have occurred over time, providing a thorough assessment of generative artificial intelligence and large language models, while we also look upon their impactful potential across different areas. The first section of the research focuses on the changes of extensive language models and generative AI, and we will try to focus upon developments in models like GPT-4 and others. These models have shown their ability number of times from applications in various sectors, from automated content generation to acurate conversational agents.
They are characterized by their capability to produce text that is both coherent and contextually appropriate. However, despite their accuracy, strengths, generative artificial intelligence and large language models face critical ethical, technological, and societal issues. Some main stream concern arises from the biases present in the training data, which can cause and lead to social inequalities.Here we looks into the causes of these biases and their implications, stressing the need for comprehensive frameworks to identify and mitigate them.
Keywords: backpropagation, bert, diffusion models, explainable ai (xai), generative ai, image synthesis, long short-term memory (lstm), natural language processing (nlp), neural network, recurrent neural network (rnn), small language model (sml), and transformer model.
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