作者批评了一种关于大型语言模型（LLM）的观点，特别是关于LLM将“正确性”和“完整性”作为潜在空间中的向量来确定的说法。这种观点认为LLM可以达到完美的预言家状态，作者认为这是荒谬的，尤其是考虑到支持者作为一名作家的职业。 AI
排序理由 该条目是一篇社交媒体帖子，表达了对LLM特定技术观点的批判性意见，而不是报道新的发展或研究。
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作者批评LLM“正确性”向量理论
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- Signal score
- 0 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Meme
- 该条目是一篇社交媒体帖子，表达了对LLM特定技术观点的批判性意见，而不是报道新的发展或研究。
- 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
- 88 days old
- Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.
完整方法见我们的编辑标准。
报道来源 [1]
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而且别以为作者只是犯了个小错误：大型语言模型（LLM）如何知道何时停止？它如何知道何时是正确的？惊人的答案是
And lest you mistake the author for making just a mild error: how does the LLM know when to stop? How does it know when it is correct? The astounding answer is that “correctness” and “completeness” and “cohesiveness” are vectors in this space, too. Any correct answer shares the s…