📄 中文摘要
大语言模型（LLMs）中的幻觉现象被认为是架构设计的一种特性，而非数据中的缺陷。这种现象源于模型在生成文本时的推理过程，模型并不总是基于真实的事实进行生成，而是可能会创造出虚构的信息。尽管这种幻觉现象可能导致不准确或误导性的输出，但它也反映了模型在处理复杂语言任务时的灵活性和创造力。理解这一特性对于改进模型的应用和减少误导性信息的传播具有重要意义。研究者们正在探索如何通过优化模型架构和训练方法来减少幻觉现象的发生，从而提高模型的可靠性和准确性。
大语言模型中的幻觉并非数据中的错误
出处: Hallucinations in LLMs Are Not a Bug in the Data
发布: 2026年3月16日
📄 English Summary
Hallucinations in LLMs Are Not a Bug in the Data
Hallucinations in large language models (LLMs) are identified as a feature of their architecture rather than a flaw in the data. This phenomenon arises from the models' reasoning processes during text generation, where they may produce fabricated information instead of relying solely on factual data. While hallucinations can lead to inaccurate or misleading outputs, they also showcase the models' flexibility and creativity in handling complex language tasks. Understanding this characteristic is crucial for improving model applications and mitigating the spread of misleading information. Researchers are exploring ways to optimize model architectures and training methods to reduce the occurrence of hallucinations, thereby enhancing the reliability and accuracy of these models.