This paper presents a comprehensive study of conversational generative artificial intelligence (AI) systems, with a primary focus on ChatGPT (GPT-4) and a systematic comparison with Google Gemini Ultra, Anthropic's Claude 3 Opus, and Meta's LLaMA 2, examining their transformer-based architectures, training methodologies including Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI (CAI), and performance across five industry-standardbenchmarks — MMLU, HumanEval, HellaSwag, GSM8K, and TruthfulQA — while also investigating the ethical challenges of hallucination, demographic bias, data privacy, adversarial misuse, and environmental cost of large-scale model training; through a systematic review of existing literature and comparative analysis, we evaluate the capabilities and limitations of these systems across real-world application domains including education, healthcare, legal practice, finance, and software engineering, ultimately finding that while these systems exhibit remarkable and often human-competitive generative and reasoning abilities, the gap between benchmark performance and reliable real-world deployment remains substantial, and that the value of conversational AI is not intrinsic but relational — shaped by how these systems are designed, contextualized, and governed — contributing to the growing body of knowledge on responsible AI development and deployment.Keywords: Generative AI, Large Language Models, ChatGPT, Claude, Gemini, Conversational AI, Natural Language Processing, RLHF, Transformer Architecture, Ethics in AI
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Chatgpt And Beyond: A Study on Conversational Generative AI Systems
Semantic Scholar · 2026
Abstract
This paper presents a comprehensive study of conversational generative artificial intelligence (AI) systems, with a primary focus on ChatGPT (GPT-4) and a systematic comparison with Google Gemini Ultra, Anthropic's Claude 3 Opus, and Meta's LLaMA 2, examining their transformer-based architectures, training methodologies including Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI (CAI), and performance across five industry-standardbenchmarks — MMLU, HumanEval, HellaSwag, GSM8K, and TruthfulQA — while also investigating the ethical challenges of hallucination, demographic bias, data privacy, adversarial misuse, and environmental cost of large-scale model training; through a systematic review of existing literature and comparative analysis, we evaluate the capabilities and limitations of these systems across real-world application domains including education, healthcare, legal practice, finance, and software engineering, ultimately finding that while these systems exhibit remarkable and often human-competitive generative and reasoning abilities, the gap between benchmark performance and reliable real-world deployment remains substantial, and that the value of conversational AI is not intrinsic but relational — shaped by how these systems are designed, contextualized, and governed — contributing to the growing body of knowledge on responsible AI development and deployment.Keywords: Generative AI, Large Language Models, ChatGPT, Claude, Gemini, Conversational AI, Natural Language Processing, RLHF, Transformer Architecture, Ethics in AI