一位 Mastodon 用户提出了一个解决方案，以解决对大型语言模型 (LLM) 置信度评分缺乏信心的问题。该用户在 Mastodon 平台上分享了该解决方案，表明这是人工智能领域的一项工作相关进展。 AI
排序理由 该条目是关于一个技术问题的社交媒体帖子，没有明确的解决方案或更广泛的影响。
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Mastodon 用户为 LLM 置信度评分提出解决方案
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
- 该条目是关于一个技术问题的社交媒体帖子，没有明确的解决方案或更广泛的影响。
- 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
- other
- 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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我们想出了一个解决方案来解决我们对LLM置信度评分没有信心的事实。🫠 #工作 #人工智能
> Here is the solution we came up with to address the fact that we don't have any confidence in the LLM confidence ratings. 🫠 # work # ai