We reveal new methods and the theoretical foundations of techniques for
editing large language models. We also show how the new theory can be used to
assess the editability of models and to expose their susceptibility to
previously unknown malicious attacks. Our theoretical approach shows that a
single metric (a specific measure of the intrinsic dimensionality of the
model's features) is fundamental to predicting the success of popular editing
approaches, and reveals new bridges between disparate families of editing
methods. We collectively refer to these approaches as stealth editing methods,
because they aim to directly and inexpensively update a model's weights to
correct the model's responses to known hallucinating prompts without otherwise
affecting the model's behaviour, without requiring retraining. By carefully
applying the insight gleaned from our theoretical investigation, we are able to
introduce a new network block -- named a jet-pack block -- which is optimised
for highly selective model editing, uses only standard network operations, and
can be inserted into existing networks. The intrinsic dimensionality metric
also determines the vulnerability of a language model to a stealth attack: a
small change to a model's weights which changes its response to a single
attacker-chosen prompt. Stealth attacks do not require access to or knowledge
of the model's training data, therefore representing a potent yet previously
unrecognised threat to redistributed foundation models. They are
computationally simple enough to be implemented in malware in many cases.
Extensive experimental results illustrate and support the method and its
theoretical underpinnings. Demos and source code for editing language models
are available at this https URL

我们揭示了关于编辑大型语言模型的新方法和理论基础，展示了新理论如何用于评估模型的可编辑性以及揭示它们对以前未知的恶意攻击的敏感性。

对大型语言模型进行可证修复或攻击的隐形编辑

Stealth edits for provably fixing or attacking large language models

Talking head synthesis, an advanced method for generating portrait videos
from a still image driven by specific content, has garnered widespread
attention in virtual reality, augmented reality and game production. Recently,
significant breakthroughs have been made with the introduction of novel models
such as the transformer and the diffusion model. Current methods can not only
generate new content but also edit the generated material. This survey
systematically reviews the technology, categorizing it into three pivotal
domains: portrait generation, driven mechanisms, and editing techniques. We
summarize milestone studies and critically analyze their innovations and
shortcomings within each domain. Additionally, we organize an extensive
collection of datasets and provide a thorough performance analysis of current
methodologies based on various evaluation metrics, aiming to furnish a clear
framework and robust data support for future research. Finally, we explore
application scenarios of talking head synthesis, illustrate them with specific
cases, and examine potential future directions.

头像合成的技术和应用进行了全面的调查和总结，涵盖了头像生成、驱动机制、编辑技术等方面，同时还提供了大量的数据集和绩效分析，以支持未来的研究和应用。

对话者合成的综合分类和分析：肖像生成、驱动机制和编辑的技术

A Comprehensive Taxonomy and Analysis of Talking Head Synthesis:  Techniques for Portrait Generation, Driving Mechanisms, and Editing

We present a method to create interpretable concept sliders that enable
precise control over attributes in image generations from diffusion models. Our
approach identifies a low-rank parameter direction corresponding to one concept
while minimizing interference with other attributes. A slider is created using
a small set of prompts or sample images; thus slider directions can be created
for either textual or visual concepts. Concept Sliders are plug-and-play: they
can be composed efficiently and continuously modulated, enabling precise
control over image generation. In quantitative experiments comparing to
previous editing techniques, our sliders exhibit stronger targeted edits with
lower interference. We showcase sliders for weather, age, styles, and
expressions, as well as slider compositions. We show how sliders can transfer
latents from StyleGAN for intuitive editing of visual concepts for which
textual description is difficult. We also find that our method can help address
persistent quality issues in Stable Diffusion XL including repair of object
deformations and fixing distorted hands. Our code, data, and trained sliders
are available at this https URL

我们提出了一种方法来创建可解释的概念滑块，从扩散模型中实现图像生成属性的精确控制。我们的方法通过识别与某个概念相对应的低秩参数方向，并最小化对其他属性的干扰来创建滑块。通过使用一小组提示或样本图像创建滑块，因此滑块方向可用于文本或视觉概念。概念滑块是即插即用的：它们可以有效组合并连续调节，实现对图像生成的精确控制。在与先前的编辑技术进行的定量实验中，我们的滑块展示了更强的目标编辑能力和较低的干扰。我们展示了天气、年龄、风格和表情的滑块，以及滑块的组合。我们展示了如何将滑块用于 StyleGAN 的潜在向量，实现对视觉概念的直观编辑（这些概念很难用文本描述）。我们还发现，我们的方法可以帮助解决 Stable Diffusion XL 中持久的质量问题，包括修复对象变形和修复扭曲的手部。我们的代码、数据和训练后的滑块可在此网址获取。