GLL: A Differentiable Graph Learning Layer for Neural Networks
The graph learning layer (GLL) replaces the usual projection head and softmax classifier with graph-Laplacian label propagation. Samples in a minibatch become nodes of a similarity graph, allowing predictions to use relationships among examples instead of classifying each representation independently.
Dec 11, 2024
Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models
This work develops a knowledge-graph-augmented language-model pipeline for analyzing long, contested narratives. Using the first season of the Serial true-crime podcast as a case study, the system organizes people, claims, events, and relationships into a structure that can be queried while preserving connections across episodes and competing accounts.
Oct 1, 2024