Feature visualization, also known as "dreaming", offers insights into vision
models by optimizing the inputs to maximize a neuron's activation or other
internal component. However, dreaming has not been successfully applied to
language models because the input space is discrete. We extend Greedy
Coordinate Gradient, a method from the language model adversarial attack
literature, to design the Evolutionary Prompt Optimization (EPO) algorithm. EPO
optimizes the input prompt to simultaneously maximize the Pareto frontier
between a chosen internal feature and prompt fluency, enabling fluent dreaming
for language models. We demonstrate dreaming with neurons, output logits and
arbitrary directions in activation space. We measure the fluency of the
resulting prompts and compare language model dreaming with max-activating
dataset examples. Critically, fluent dreaming allows automatically exploring
the behavior of model internals in reaction to mildly out-of-distribution
prompts. Code for running EPO is available at
this https URL A companion page demonstrating
code usage is at this https URL

采用进化提示优化算法来实现语言模型的流畅梦幻，从而同时最大化所选内部特征和提示流畅性之间的帕累托前沿。

语言模型的流利梦境

Fluent dreaming for language models

Dreaming is a fundamental but not fully understood part of human experience
that can shed light on our thought patterns. Traditional dream analysis
practices, while popular and aided by over 130 unique scales and rating
systems, have limitations. Mostly based on retrospective surveys or lab
studies, they struggle to be applied on a large scale or to show the importance
and connections between different dream themes. To overcome these issues, we
developed a new, data-driven mixed-method approach for identifying topics in
free-form dream reports through natural language processing. We tested this
method on 44,213 dream reports from Reddit's r/Dreams subreddit, where we found
217 topics, grouped into 22 larger themes: the most extensive collection of
dream topics to date. We validated our topics by comparing it to the
widely-used Hall and van de Castle scale. Going beyond traditional scales, our
method can find unique patterns in different dream types (like nightmares or
recurring dreams), understand topic importance and connections, and observe
changes in collective dream experiences over time and around major events, like
the COVID-19 pandemic and the recent Russo-Ukrainian war. We envision that the
applications of our method will provide valuable insights into the intricate
nature of dreaming.

开发了一种新的、数据驱动的混合方法，用于识别梦中话题，可应用于自由度大、关联性强的主题。测试结果显示此方法能识别出与大规模相关的主题，能观察到重要性和连结性的变化。