English(EN) Which local model is actually good at knowing when to stop and ask you a question?

用户寻求能不确定时提问的本地LLM

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-08 10:17

一位Reddit r/LocalLLaMA板块的用户正在寻找本地大型语言模型（LLM）的推荐，这些模型擅长识别歧义并提出澄清性问题，而不是基于可能不正确的假设继续进行。用户指出，虽然许多模型都擅长自主运行，但在需求不明确时，这可能会导致徒劳无功。他们有兴趣了解这种能力主要是模型本身、系统提示还是所使用的代理框架的功能。 AI

影响识别出当前LLM评估指标中的一个空白，可能影响未来的模型开发和基准测试。

排序理由用户在关于特定模型能力的subreddit上的提问。

在 r/LocalLLaMA 阅读 →

Local LLaMA

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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: Commentary; 用户在关于特定模型能力的subreddit上的提问。
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: product, 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: High; Clearly on-topic for AI-industry coverage.
Story freshness: 1 days old; Coverage has settled into its steady-state source set.

完整方法见我们的编辑标准。

报道来源 [1]

r/LocalLLaMA TIER_1 English(EN) · /u/sunychoudhary · 2026-09-08 10:17

哪个本地模型擅长知道何时停止并向您提问？

<div class="md">I’ve been thinking about this after using more agentic/local coding models. A lot of the newer models are surprisingly good at continuing on their own. But sometimes that seems like the problem. If a requirement is ambiguous,…

本文如何被排名

报道来源 [1]

哪个本地模型擅长知道何时停止并向您提问？

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