Pre-trained large language models can perform natural language processing
downstream tasks by conditioning on human-designed prompts. However, a
prompt-based approach often requires "prompt engineering" to design different
prompts, primarily hand-crafted through laborious trial and error, requiring
human intervention and expertise. It is a challenging problem when constructing
a prompt-based keyphrase extraction method. Therefore, we investigate and study
the effectiveness of different prompts on the keyphrase extraction task to
verify the impact of the cherry-picked prompts on the performance of extracting
keyphrases. Extensive experimental results on six benchmark keyphrase
extraction datasets and different pre-trained large language models demonstrate
that (1) designing complex prompts may not necessarily be more effective than
designing simple prompts; (2) individual keyword changes in the designed
prompts can affect the overall performance; (3) designing complex prompts
achieve better performance than designing simple prompts when facing long
documents.

通过实验证明，设计复杂的提示可能并不一定比设计简单的提示更有效，设计的提示中的关键词的改变会影响整体性能，在面对长文档时，设计复杂的提示可以获得更好的性能。

基于提示的无监督关键词提取的初步实证研究

A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase  Extraction

Recent text-to-3D generation methods achieve impressive 3D content creation
capacity thanks to the advances in image diffusion models and optimizing
strategies. However, current methods struggle to generate correct 3D content
for a complex prompt in semantics, i.e., a prompt describing multiple
interacted objects binding with different attributes. In this work, we propose
a general framework named Progressive3D, which decomposes the entire generation
into a series of locally progressive editing steps to create precise 3D content
for complex prompts, and we constrain the content change to only occur in
regions determined by user-defined region prompts in each editing step.
Furthermore, we propose an overlapped semantic component suppression technique
to encourage the optimization process to focus more on the semantic differences
between prompts. Extensive experiments demonstrate that the proposed
Progressive3D framework generates precise 3D content for prompts with complex
semantics and is general for various text-to-3D methods driven by different 3D
representations.

最新的文本到 3D 生成方法通过图像扩散模型和优化策略的进展实现了令人印象深刻的 3D 内容创建能力。然而，当前的方法在生成复杂语义的准确 3D 内容（即描述多个相互作用的对象与不同属性绑定的提示）方面存在困难。在这项工作中，我们提出了一个名为 Progressive3D 的通用框架，将整个生成过程分解为一系列局部逐步编辑步骤，以创建复杂提示的精确 3D 内容，并在每个编辑步骤中，将内容更改限制在由用户定义的区域提示确定的区域内。此外，我们提出了一种重叠语义组件抑制技术，以鼓励优化过程更关注提示之间的语义差异。大量实验证明了所提出的 Progressive3D 框架能够为具有复杂语义的提示生成精确的 3D 内容，并且适用于由不同 3D 表示驱动的各种文本到 3D 方法。

Progressive3D：逐步局部编辑复杂语义提示的 3D 内容创作

Progressive3D: Progressively Local Editing for Text-to-3D Content  Creation with Complex Semantic Prompts

Emerging large-scale text-to-image generative models, e.g., Stable Diffusion
(SD), have exhibited overwhelming results with high fidelity. Despite the
magnificent progress, current state-of-the-art models still struggle to
generate images fully adhering to the input prompt. Prior work, Attend &
Excite, has introduced the concept of Generative Semantic Nursing (GSN), aiming
to optimize cross-attention during inference time to better incorporate the
semantics. It demonstrates promising results in generating simple prompts,
e.g., ``a cat and a dog''. However, its efficacy declines when dealing with
more complex prompts, and it does not explicitly address the problem of
improper attribute binding. To address the challenges posed by complex prompts
or scenarios involving multiple entities and to achieve improved attribute
binding, we propose Divide & Bind. We introduce two novel loss objectives for
GSN: a novel attendance loss and a binding loss. Our approach stands out in its
ability to faithfully synthesize desired objects with improved attribute
alignment from complex prompts and exhibits superior performance across
multiple evaluation benchmarks. More videos and updates can be found on the
project page https://sites.google.com/view/divide-and-bind.

提出了一种名为 'Divide & Bind' 的方法，用于处理复杂的提示或涉及多个实体的情景，并实现改进的属性绑定，以忠实地合成所需的对象并在多个评估基准上展示卓越性能。