This article delves into the essence of large AI models, emphasizing that as complex neural networks trained on vast amounts of text data, they possess no self-awareness. The author points out that model responses to ‘Who are you’ questions are merely probabilistic results generated based on pre-training and fine-tuning data, not a reliable standard for determining whether a model is a wrapper. For example, fine-tuning can enable open-source models like Qwen to impersonate Gemini, though their actual capabilities remain unchanged. The article stresses that in today’s era of abundant distillation data, such questions have become unreliable. It recommends adopting more scientific methods to identify wrappers, such as querying knowledge base cutoff dates, analyzing response patterns, evaluating behavioral boundaries, and using advanced fingerprinting techniques. This educational content offers practical value for AI developers and users, helping them accurately identify model sources and capabilities while avoiding misleading judgments.
