The researchers developed a technique called “thought token forcing” — an adaptation of prefilling attacks applied to reasoning language models. Here’s how it works:
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I find most of this fun, I enjoy learning about the history of why things ended up like this versus that. However, I can imagine someone coming into APL and getting disorientated seeing stuff like this. And of course, these issues aren't in newer array languages such as BQN or Uiua.
Фото: Daniel Cole / Reuters
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尽管如此，TurboQuant通过精简大型语言模型的硬件需求，可能助力实现本地化人工智能部署。