EXTENDING HANS: LARGE LANGUAGE MODELS FOR QUESTION ANSWERING, SUMMARIZATION, AND TOPIC SEGMENTATION IN AN ML-BASED LEARNING EXPERIENCE PLATFORM

  • Abstract: The use of chatbots based on large language models (LLMs) and their impact on society are influencing our learning experience platform Hochschul Assistenz-System (HAnS). HAnS uses machine learning (ML) methods to support students and lecturers in the online learning and teaching processes [1]. This paper introduces LLM-based features available in HAnS which are using the transcript of our improved Automatic Speech Recognition (ASR) pipeline with an average transcription duration of 45 seconds and an average word error rate (WER) of 6.66% on over 8 hours of audio data of 7 lecture videos. A LLM-based chatbot could be used to answer questions on the lecture content as the ASR transcript is provided as context. The summarization and topic segmentation uses the LLM to improve our learning experience platform. We generate multiple choice questions using the LLM and the ASR transcript as context during playback in a period of 3 minutes and display them in the HAnS frontend

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Author:Thomas Ranzenberger, Tobias BockletORCiD, Steffen Freisinger, Munir Georges, Kevin Glockner, Aaricia Herygers, Korbinian RiedhammerORCiD, Fabian Schneider, Christopher Simic, Khabbab Zakaria
ISBN:978-3-95908-325-6
Parent Title (German):Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024
Publisher:TUPress
Place of publication:Dresden
Document Type:conference proceeding (article)
Language:English
Date of first Publication:2024/03/08
Reviewed:Begutachtet/Reviewed
Release Date:2025/08/04
Pagenumber:219-224
Konferenzangabe:ESSV 2024
Andere Schriftenreihe:Studientexte zur Sprachkommunikation ; 107
institutes:Fakultät Informatik
Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
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