Contextual Categorization Enhancement through LLMs Latent-Space
This addresses the complexity and cost of categorization for database administrators, but appears incremental as it builds on existing transformer and graph-based methods.
The paper tackles the problem of managing semantic quality in large textual datasets like Wikipedia by leveraging transformer models to distill semantic information into a latent space, using approaches like Convex Hull and HNSWs to enhance category identity and retrieve high-RP items for recommendations and outlier detection.
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.