English(EN) I tried reversing chat templates to see which models are the best at generating user turns

Mistral Small 3 24B 在反转聊天模板测试中擅长模仿人类文本

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 14:29

一项实验探讨了指令微调的大型语言模型在被提示扮演用户时生成人类文本的能力。研究发现，使用反转聊天模板（模型生成用户回合）比角色扮演系统提示更有效。在约 30 个测试模型中，Mistral Small 3 24B 在此方面模仿人类行为的表现最佳。 AI

影响这项研究提出了一种评估和潜在改进 LLM 生成自然、类人对话能力的新方法。

排序理由通过新颖方法对 LLM 行为进行研究。 [lever_c_demoted from research: ic=1 ai=1.0]

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: Tool; 通过新颖方法对 LLM 行为进行研究。 [lever_c_demoted from research: ic=1 ai=1.0]
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: model release; 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: 11 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。

r/LocalLLaMA TIER_1 English(EN) · /u/EntropyMagnets · 2026-10-22 14:29

我尝试反向生成聊天模板，以了解哪些模型最擅长生成用户发言

<div class="md"><p>I tried for fun to see what happens if we let instruction-tuned LLMs generate the text that should come from the user (so after the <code>user_start</code> special token). I immediately noticed that some models were way better than others.</p> <p…