How is the interpretability of generative search results?

Generative search results typically have low interpretability because their content is dynamically generated by AI models rather than directly引用 fixed sources, making it difficult to trace the information sources, reasoning logic, and factual basis. Transparency of information sources: Generated content often does not clearly indicate the original source, making it impossible for users to directly verify the reliability of the information. For example, AI may generate answers by synthesizing content from multiple web pages but does not display specific citation links. Visibility of reasoning process: The "black box" nature of AI models makes the intermediate logical chain opaque, making it difficult for users to understand how conclusions are derived from input data. For instance, it is impossible to know whether a certain viewpoint is based on statistical laws or specific cases. Difficulty in fact-checking: Content generated by fusing multi-source information may contain hidden errors, and users lack the basis for point-by-point verification. For example, numerical or time expressions may have errors due to biases in the model training data. To enhance trust in generated results, users are advised to assist in judging the reliability of generated content by cross-referencing information from different sources and paying attention to credibility indicators marked by the platform (such as source links or fact-check labels).