发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究识别出大型语言模型等生成离散概率分布的神经网络存在歧义诅咒，即下一个词元分布歧义性越高越难准确学习，经理论分析与实验验证，为理解模型统计能力及信任其输出分布提供了新视角与实用框架。
AI 中文摘要
大型语言模型越来越依赖采样来推动自身改进，这使得它们所学分布的保真度比以往任何时候都更为关键。然而，并非所有分布都同等易于学习。在本研究中，我们识别出一种歧义诅咒：在大型语言模型以及更广泛的所有生成离散概率分布的神经网络中，下一个词元分布的歧义性越高，就越难以准确学习。通过广泛的理论分析，我们将这种诅咒追溯至架构与学习层面的根源：更具歧义性的分布需要更多的容量来存储、更大的嵌入来表征、更多的步骤来拟合，且会放大词元采样噪声。我们在具有受控真值的合成任务上验证了这些发现，并在基于真实数据训练的语言模型中观察到了相同特征。我们的结果为大型语言模型的统计能力提供了新视角，并为何时信任其输出分布提供了实用框架。
英文摘要
Large language models increasingly rely on sampling as a driver of their own improvement, making the fidelity of their learned distributions more critical than ever. Yet, not all distributions are equally easy to learn. In this work, we identify a curse of ambiguity: in large language models, and more broadly in all neural networks that produce discrete probability distributions, the more ambiguous a next-token distribution is, the harder it is to learn accurately. Through an extensive theoretical analysis, we trace this curse to architectural and learning roots. More ambiguous distributions require more capacity to be stored, larger embeddings to be represented, more steps to be fitted, and amplify token-sampling noise. We validate these findings on synthetic tasks with controlled ground truth and observe the same signatures in language models trained on real data. Our results provide a new perspective on the statistical capabilities of large language models and a practical framework for when to trust their output distribution.
