Core Viewpoint - The release of GPT-5 has led to significant criticism, with users claiming it has become less creative and more rigid in its responses compared to previous versions [1][2][3]. Group 1: Model Characteristics and User Feedback - GPT-5 has a significantly reduced hallucination rate, which has made its outputs appear more rigid and less dynamic, particularly affecting its performance in creative writing tasks [3][5][10]. - Users have expressed dissatisfaction with GPT-5's responses, describing them as dull and lacking emotional depth, despite improvements in areas like mathematics and science [9][10]. - The model's requirement for detailed prompts to generate satisfactory outputs has been seen as a regression for users accustomed to more intuitive interactions with earlier versions [3][9]. Group 2: Hallucination and Its Implications - Hallucination in AI models refers to the generation of content that does not align with human experience, and it is categorized into five types, including language generation errors and logical reasoning mistakes [14][17]. - The industry has recognized that completely eliminating hallucinations is impossible, and there is a need to view the impact of hallucinations in a nuanced manner [10][11][12]. - The perception of hallucinations has shifted from being viewed solely as a negative issue to a more balanced understanding of their potential utility in certain contexts [131]. Group 3: Mitigation Strategies - Current strategies to mitigate hallucinations include using appropriate models, In-Context Learning, and fine-tuning techniques, with varying degrees of effectiveness [30][31][32]. - The use of Retrieval-Augmented Generation (RAG) is prevalent in high-precision industries like healthcare and finance, although it can significantly increase computational costs [35][46]. - In-Context Learning has shown promise in reducing hallucination rates but faces challenges related to the quality and structure of the context provided [70][72]. Group 4: Industry Trends and Perspectives - The industry has moved towards a more rational understanding of hallucinations, recognizing that some scenarios may tolerate them while others cannot [131]. - There is a growing acknowledgment that traditional machine learning methods still hold advantages in complex reasoning tasks compared to large language models [107][108]. - The trend indicates a shift towards integrating traditional machine learning techniques with large language models to enhance their capabilities and mitigate hallucination issues [108][109].