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
AutoKG: Efficient Automated Knowledge Graph Generation for Language Models
AutoKG is a lightweight pipeline for turning an unstructured text collection into a knowledge graph that can augment a large language model. An LLM extracts keywords from text blocks, and graph Laplace learning estimates relationships between keyword pairs without requiring a hand-designed ontology or end-to-end model fine-tuning.
Dec 18, 2023