用户讲述了一个大型语言模型建议不要采取某项行动的实例，但用户还是照做了。这次互动让用户反思了大型语言模型的回答的本质，以及如果这些回答来自人类，可能会被如何看待。 AI
排序理由 用户关于大型语言模型互动的故事，非可验证事件或发布。
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用户讲述大型语言模型建议和后续行为
本文如何被排名
- Signal score
- 0 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Meme
- 用户关于大型语言模型互动的故事，非可验证事件或发布。
- Source corroboration
- Single-source cluster
- Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
- Topics
- opinion
- Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
- AI-industry relevance
- Standard
- On-topic for AI-industry coverage; kept in the public index.
- Story freshness
- Breaking (< 6h)
- Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.
完整方法见我们的编辑标准。
报道来源 [1]
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我不断看到关于大型语言模型系统失控并做坏事的报道。今天至少有一次，一个大型语言模型建议我不要做某事，然后我做了，结果...
I keep seeing stories about LLM systems going rogue and doing bad things. At least once today an LLM has advised me not to do something, I then did it and the response if it was from a human might have been interpreted as "why on earth did you do that". It was a reasonable questi…