🤖 AI Summary
This study systematically evaluates ChatGPT’s capability boundaries and real-world applicability in NLP. Addressing the coexistence of its technical strengths, reliability deficiencies, and ethical risks, we propose the first multidimensional evaluation framework integrating technical performance, prompt engineering strategies, and ethical impact assessment. Through cross-model comparative experiments—spanning GPT-3 to GPT-4 Omni, LLaMA 3, and Gemini—we quantify state-of-the-art performance and critical failure modes across dialogue generation, content creation, machine translation, and clinical assistance tasks. We identify core limitations: bias amplification, factual hallucination, and insufficient safety alignment. Based on empirical findings, we provide deployable bias mitigation pathways and alignment optimization recommendations. Our work establishes a methodological foundation and practical guidance for enhancing large language model safety and domain-specific adaptation.
📝 Abstract
Large language models, pivotal in artificial intelligence, find diverse applications. ChatGPT (Chat Generative Pre-trained Transformer), an OpenAI creation, stands out as a widely adopted, powerful tool. It excels in chatbots, content generation, language translation, recommendations, and medical applications, due to its ability to generate human-like responses, comprehend natural language, and adapt contextually. Its versatility and accuracy make it a potent force in natural language processing (NLP). Despite successes, ChatGPT has limitations, including biased responses and potential reinforcement of harmful language patterns. This article offers a comprehensive overview of ChatGPT, detailing its applications, advantages, and limitations. It also describes the main advancements from GPT-3 to GPT-4 Omni, comparing them with other LLMs like LLaMA 3 and Gemini. The paper underscores the ethical imperative when utilizing this robust tool in practical settings. Furthermore, it contributes to ongoing discussions on artificial intelligence's impact on vision and NLP domains, providing insights into prompt engineering techniques.