Leveraging Large Language Models for content analysis and generation for podcast transcriptions

Ghaly, Michael Magdy Nasr Zaki (2023) Leveraging Large Language Models for content analysis and generation for podcast transcriptions. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270], Documento full-text non disponibile
Il full-text non è disponibile per scelta dell'autore. (Contatta l'autore)


This research project explores podcast transcription analysis and content generation, leveraging Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) framework. It examines a wide range of natural language processing (NLP) tasks, from summarization to question-answering, chapter segmentation, sentiment analysis, and more, in order to provide a comprehensive analysis of podcast content. The project aims to overcome the context window limitations of LLMs by introducing RAG. Ultimately, it seeks to enhance the accessibility, searchability, and engagement of podcast experiences, with potential applications beyond the podcast domain.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Ghaly, Michael Magdy Nasr Zaki
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Large Language Models,LLMs,Natural Language Processing,NLP,Retrieval-Augmented Generation,RAG,Summarization,Topic Segmentation,Question-Answering,Context Window,Prompt Engineering,Musixmatch,Podcasts,Podcast Transcriptions
Data di discussione della Tesi
21 Ottobre 2023

Altri metadati

Gestione del documento: Visualizza il documento