新的鞅理论分析LLM生成动态

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一个新的理论框架来分析大型语言模型（LLM）生成的随机动态。他们将每步预测误差定义为鞅，证明其累积误差过程具有零的条件一阶矩。该工作还推导出了二阶行为的闭式表达式，将序列波动与预测不确定性联系起来。在Llama-3.x和Qwen2.5模型上的实验验证了这些理论预测，为分析LLM解码、识别模型指纹以及推进LLM生成文本检测和不确定性量化提供了一种概率方法。 AI

影响提供了一个理解LLM生成的新概率框架，可能改进文本检测和不确定性量化。

排序理由该集群包含一篇学术论文，详细介绍了分析LLM生成动态的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Xiahui Zhu, Zhenyi Qi, Wanwei Liu, Haoang Chi, Yang Tan, Xinyu Sun, Ji Wang · 2026-10-15 04:00

自回归解码作为鞅：LLM生成随机动力学的特征描述

arXiv:2610.16041v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success across a wide range of language tasks. At their core, autoregressive LLMs are conditional probability models that learn to approximate the next-token distribution given…

本文如何被排名

报道来源 [1]

自回归解码作为鞅：LLM生成随机动力学的特征描述

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