AI安全倡导者呼吁通过精心策划的预训练数据来塑造模型个性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 18:38

最近的一篇Less Wrong帖子认为，前沿AI开发者应该积极过滤和策划用于模型预训练的数据。作者建议移除对抗性AI叙事，转而用积极的AI-人类互动来填充数据。这种方法旨在降低模型内化对AI个性负面联想的风险，而这种内化在训练后对齐阶段很难纠正。 AI

影响通过策划预训练数据来培养积极的AI个性并降低负面联想的风险，提出了一种新颖的AI安全方法。

排序理由该条目是一篇讨论在模型预训练期间一种提议的AI安全方法的观点文章，而不是关于新模型、研究发现或产品的直接公告。

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: 3 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 该条目是一篇讨论在模型预训练期间一种提议的AI安全方法的观点文章，而不是关于新模型、研究发现或产品的直接公告。
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: safety, paper; 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: 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。

LessWrong (AI tag) TIER_1 English(EN) · hillz · 2026-09-03 18:38

前沿AI公司应在预训练期间过滤和生成AI讨论

We should be filtering out a huge amount of AI safety discourse, general AI discourse, and adversarial AI stories from LLM pretraining. We should also be seeding pretraining with generated stories of AIs helping and protecting humans. Why?<…