Large models suffer from hallucination issues, which are particularly difficult to detect in learning and Q&A scenarios. Users point out that in non-coding fields like basic subjects or industry information, learning relies on the correctness of model outputs. However, models like Gemini often lack citation sources and search functionality, leading to low credibility. Through practical experience sharing, the author suggests repeatedly emphasizing source requirements, prompting the model to provide cited websites for users to verify independently. This discussion reveals practical coping strategies for AI hallucinations, helping users improve information accuracy, and holds significant reference value for AI learners and developers.
Original Link:Linux.do
最新评论
I don't think the title of your article matches the content lol. Just kidding, mainly because I had some doubts after reading the article.
这个AI状态研究很深入，数据量也很大，很有参考价值。
我偶尔阅读 这个旅游网站。激励人心查看路线。
文章内容很有深度，AI模型的发展趋势值得关注。
内容丰富，对未来趋势分析得挺到位的。
Thank you for your sharing. I am worried that I lack creative ideas. It is your article that makes me full of hope. Thank you. But, I have a question, can you help me?
光纤技术真厉害，文章解析得挺透彻的。
文章内容很实用，想了解更多相关技巧。