LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs

Abstract

This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and validatable cross-cultural lyric analysis. The core component, Sequential Language Model Integration (SLMI), enhances the interpretability and reliability of transformer-based LLMs by addressing explainability and validation challenges through Retrieval-Augmented Generation (RAG), hybrid search, and rule-based evaluation. An important feature of LYRICEL is its use of KG visualizations, which serve as dynamic links to improve interpretability and validatability by structuring data relationships and sources. These visualizations are central to advancements in four areas: KG representation learning, knowledge acquisition, temporal KGs, and knowledge-aware applications. Tested on Greek folk music with models like GPT-4o and BERT, LYRICEL’s trustworthiness is assessed using the VIRTSI model, which quantifies cognitive trust in human-computer interactions. The framework shows strong potential for cross-cultural applications, particularly in languages such as Modern Greek which encompasses a rich cultural heritage spanning centuries of history and traditions resulting in a complex study. The outcomes of GPT-enabled LYRICEL are compared to ChatGPT alone and show a significant improvement in the reliability and efficiency of interactions that can reach a global audience, enhancing the accessibility and understanding of diverse cultural heritages.