Contextual Categorization Enhancement through LLMs Latent-Space
https://papers.cool/arxiv/2404.16442
Authors: Zineddine Bettouche ; Anas Safi ; Andreas Fischer
Summary: Managing the semantic quality of the categorization in large textual datasets, such as Wikipedia, presents significant challenges in terms of complexity and cost. In this paper, we propose leveraging transformer models to distill semantic information from texts in the Wikipedia dataset and its associated categories into a latent space. We then explore different approaches based on these encodings to assess and enhance the semantic identity of the categories. Our graphical approach is powered by Convex Hull, while we utilize Hierarchical Navigable Small Worlds (HNSWs) for the hierarchical approach. As a solution to the information loss caused by the dimensionality reduction, we modulate the following mathematical solution: an exponential decay function driven by the Euclidean distances between the high-dimensional encodings of the textual categories. This function represents a filter built around a contextual category and retrieves items with a certain Reconsideration Probability (RP). Retrieving high-RP items serves as a tool for database administrators to improve data groupings by providing recommendations and identifying outliers within a contextual framework.
Contextual Categorization Enhancement through LLMs Latent-Space
Authors: Zineddine Bettouche ; Anas Safi ; Andreas Fischer
Summary: Managing the semantic quality of the categorization in large textual datasets, such as Wikipedia, presents significant challenges in terms of complexity and cost. In this paper, we propose leveraging transformer models to distill semantic information from texts in the Wikipedia dataset and its associated categories into a latent space. We then explore different approaches based on these encodings to assess and enhance the semantic identity of the categories. Our graphical approach is powered by Convex Hull, while we utilize Hierarchical Navigable Small Worlds (HNSWs) for the hierarchical approach. As a solution to the information loss caused by the dimensionality reduction, we modulate the following mathematical solution: an exponential decay function driven by the Euclidean distances between the high-dimensional encodings of the textual categories. This function represents a filter built around a contextual category and retrieves items with a certain Reconsideration Probability (RP). Retrieving high-RP items serves as a tool for database administrators to improve data groupings by providing recommendations and identifying outliers within a contextual framework.
想象一下，维基百科是一个巨大的图书馆，里面有数百万本书，但没有图书管理员来整理它们。找到你想要的信息就像大海捞针一样困难。这就是为什么维基百科的分类如此重要，它帮助我们快速找到我们需要的信息。
但是，随着维基百科内容的不断增长，传统的分类方法开始力不从心。人工分类费时费力，而简单的计算机算法又无法理解文章的真正含义。
人工智能来帮忙！
科学家们正在尝试用人工智能（AI）来解决这个问题。他们使用一种叫做“变换器模型”的AI技术，它可以像人一样理解语言的含义。
AI如何工作？
这项技术有什么好处？
未来展望
科学家们还在不断改进这项技术，希望未来能够：
总而言之，AI正在帮助我们构建一个更智能、更易用的维基百科，让每个人都能轻松获取知识。