In a physical system, changing parameters such as temperature can induce a
phase transition: an abrupt change from one state of matter to another.
Analogous phenomena have recently been observed in large language models.
Typically, the task of identifying phase transitions requires human analysis
and some prior understanding of the system to narrow down which low-dimensional
properties to monitor and analyze. Statistical methods for the automated
detection of phase transitions from data have recently been proposed within the
physics community. These methods are largely system agnostic and, as shown
here, can be adapted to study the behavior of large language models. In
particular, we quantify distributional changes in the generated output via
statistical distances, which can be efficiently estimated with access to the
probability distribution over next-tokens. This versatile approach is capable
of discovering new phases of behavior and unexplored transitions -- an ability
that is particularly exciting in light of the rapid development of language
models and their emergent capabilities.

通过统计距离量化输出生成中的分布变化，本研究利用物理学界提出的自动检测相变的统计方法，成功地将其应用于大型语言模型中，以发现新的行为状态和未探索的转变。

大型语言模型输出分布的相变

Phase Transitions in the Output Distribution of Large Language Models

Generative models are invaluable in many fields of science because of their
ability to capture high-dimensional and complicated distributions, such as
photo-realistic images, protein structures, and connectomes. How do we evaluate
the samples these models generate? This work aims to provide an accessible
entry point to understanding popular notions of statistical distances,
requiring only foundational knowledge in mathematics and statistics. We focus
on four commonly used notions of statistical distances representing different
methodologies: Using low-dimensional projections (Sliced-Wasserstein; SW),
obtaining a distance using classifiers (Classifier Two-Sample Tests; C2ST),
using embeddings through kernels (Maximum Mean Discrepancy; MMD), or neural
networks (Fr\'echet Inception Distance; FID). We highlight the intuition behind
each distance and explain their merits, scalability, complexity, and pitfalls.
To demonstrate how these distances are used in practice, we evaluate generative
models from different scientific domains, namely a model of decision making and
a model generating medical images. We showcase that distinct distances can give
different results on similar data. Through this guide, we aim to help
researchers to use, interpret, and evaluate statistical distances for
generative models in science.

本研究旨在理解和评估用于科学中生成模型的统计距离，包括 Sliced-Wasserstein、Classifier Two-Sample Tests、Maximum Mean Discrepancy 等方法。通过评估决策模型和生成医学图像模型，展示了这些不同距离对相似数据的不同结果。