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在人工智能迅猛发展的今天，语言模型的效率与性能成为了研究者们关注的焦点。近期，腾讯微信AI联合清华大学发布了一项引人注目的研究成果——连续自回归语言模型（CALM），这一新模型的提出不仅打破了传统语言模型的局限，还为未来的语言处理技术提供了全新的视角。

微信AI联合清华大学发布了连续自回归语言模型（CALM），这一创新性技术引发了业界广泛关注。 传统的大型语言模型（LLM）依赖于预测下一个token（词元），虽然保证了连贯性，但也带来了高昂的计算成本和响应延迟。CALM的出现，旨在解决LLM效率瓶颈，为构建更高效的语言模型提供了新的思路。 CALM的核心理念：从离散到连续 CALM的核心在于将语言建模从预测离散的token，转向预测连续的向量。

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众所周知，大型语言模型（LLM）的根本运作方式是预测下一个 token（词元），能够保证生成的连贯性和逻辑性，但这既是 LLM 强大能力的「灵魂」所在，也是其枷锁，将导致高昂的计算成本和响应延迟。可以说，业界「苦」LLM ...

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