In an era where the volume of data drives the effectiveness of
self-supervised learning, the specificity and clarity of data semantics play a
crucial role in model training. Addressing this, we introduce HYPerbolic
Entailment filtering (HYPE), a novel methodology designed to meticulously
extract modality-wise meaningful and well-aligned data from extensive, noisy
image-text pair datasets. Our approach leverages hyperbolic embeddings and the
concept of entailment cones to evaluate and filter out samples with meaningless
or underspecified semantics, focusing on enhancing the specificity of each data
sample. HYPE not only demonstrates a significant improvement in filtering
efficiency but also sets a new state-of-the-art in the DataComp benchmark when
combined with existing filtering techniques. This breakthrough showcases the
potential of HYPE to refine the data selection process, thereby contributing to
the development of more accurate and efficient self-supervised learning models.
Additionally, the image specificity $\epsilon_{i}$ can be independently applied
to induce an image-only dataset from an image-text or image-only data pool for
training image-only self-supervised models and showed superior performance when
compared to the dataset induced by CLIP score.

本研究介绍了一种名为 HYPerbolic Entailment filtering (HYPE) 的新方法，利用超伸缩嵌入和蕴含锥的概念，精确提取具有明确语义的图像 - 文本对数据集，以增强数据样本的特异性。HYPE 在数据筛选效率方面取得了显著的改进，并在与现有筛选技术相结合时创下了 DataComp 基准的最新记录。该研究突破了数据选择过程的潜力，为更准确高效的自监督学习模型的发展做出了贡献。

HYPE: 超伪定型图像与文本的包含性过滤

HYPE: Hyperbolic Entailment Filtering for Underspecified Images and  Texts

Systems that support users in the automatic creation of visualizations must
address several subtasks - understand the semantics of data, enumerate relevant
visualization goals and generate visualization specifications. In this work, we
pose visualization generation as a multi-stage generation problem and argue
that well-orchestrated pipelines based on large language models (LLMs) and
image generation models (IGMs) are suitable to addressing these tasks. We
present LIDA, a novel tool for generating grammar-agnostic visualizations and
infographics. LIDA comprises of 4 modules - A SUMMARIZER that converts data
into a rich but compact natural language summary, a GOAL EXPLORER that
enumerates visualization goals given the data, a VISGENERATOR that generates,
refines, executes and filters visualization code and an INFOGRAPHER module that
yields data-faithful stylized graphics using IGMs. LIDA provides a python api,
and a hybrid user interface (direct manipulation and natural language) for
interactive chart, infographics and data story generation.

该论文介绍了一个名为 LIDA 的工具，它采用基于大型语言模型和图像生成模型的管道，将可视化生成形式化为一个多阶段生成问题，能够支持数据和目标的语义理解、可视化目标枚举和可视化规范的生成，LIDA 提供了 Python API 和混合用户界面用于交互式图表、信息图表和数据故事生成。

LIDA：利用大型语言模型自动生成与语法无关的视觉化和信息图表工具

LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models

Within the current context of Information Societies, large amounts of
information are daily exchanged and/or released. The sensitive nature of much
of this information causes a serious privacy threat when documents are
uncontrollably made available to untrusted third parties. In such cases,
appropriate data protection measures should be undertaken by the responsible
organization, especially under the umbrella of current legislations on data
privacy. To do so, human experts are usually requested to redact or sanitize
document contents. To relieve this burdensome task, this paper presents a
privacy model for document redaction/sanitization, which offers several
advantages over other models available in the literature. Based on the
well-established foundations of data semantics and the information theory, our
model provides a framework to develop and implement automated and inherently
semantic redaction/sanitization tools. Moreover, contrary to ad-hoc redaction
methods, our proposal provides a priori privacy guarantees which can be
intuitively defined according to current legislations on data privacy.
Empirical tests performed within the context of several use cases illustrate
the applicability of our model and its ability to mimic the reasoning of human
sanitizers.

本文针对当前信息社会的敏感性信息进行隐私保护的问题，提出了一种基于数据语义和信息理论的文档信息去标识化模型，并通过多种应用案例的实证测试，证明其具备可行性和与人工操作相近的功效。