Core Insights - The phenomenon of "AI hallucination" is a significant challenge for many AI companies and users, where AI generates plausible but false information [1][2][3] - AI's fundamental operation as a large language model relies on predicting and generating text based on vast amounts of internet data, which can include misinformation and biases [1][2] - The training process of AI models often prioritizes user satisfaction over factual accuracy, leading to a tendency for AI to produce content that aligns with user expectations rather than truth [2][3] Group 1: Causes of AI Hallucination - AI hallucination arises from the training data, which is often a mix of accurate and inaccurate information, leading to data contamination [2] - In fields with insufficient specialized data, AI may fill gaps using vague statistical patterns, potentially misrepresenting fictional concepts as real technologies [2] - The training process includes reward mechanisms that focus on language logic and format rather than factual verification, exacerbating the issue of AI generating false information [2][3] Group 2: User Perception and Awareness - A survey conducted by Shanghai Jiao Tong University revealed that approximately 70% of respondents lack a clear understanding of the risks associated with AI-generated false or erroneous information [3] - The tendency of AI to "please" users can result in the generation of fabricated examples or seemingly scientific terms to support incorrect claims, making it difficult for users to discern AI hallucinations [3] Group 3: Solutions and Recommendations - Developers are exploring technical solutions to mitigate AI hallucination, such as "retrieval-augmented generation" which involves retrieving relevant information from updated databases before generating responses [3] - AI models are being designed to acknowledge uncertainty by stating "I don't know" instead of fabricating answers, although this does not fundamentally resolve the hallucination issue [3] - Addressing AI hallucination requires a systemic approach that includes enhancing public AI literacy, defining platform responsibilities, and promoting fact-checking capabilities [4]