Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations
Donisch, Leo; Schacht, Sigurd; Lanquillon, Carsten (2024)
Arxiv.
DOI: 10.48550/arXiv.2408.03130
Open Access
An approach to optimize inference of the DIART speaker diarization pipeline
Aperdannier, Roman; Schacht, Sigurd; Piazza, Alexander (2024)
Arxiv.
DOI: 10.48550/arXiv.2408.02341
Open Access
Systematic Evaluation of Online Speaker Diarization Systems Regarding their Latency
Aperdannier, Roman; Schacht, Sigurd; Piazza, Alexander (2024)
Arxiv.
DOI: 10.48550/arXiv.2407.04293
Open Access
A Review of Common Online Speaker Diarization Methods
Aperdannier, Roman; Schacht, Sigurd; Piazza, Alexander (2024)
Arxiv.
DOI: 10.48550/arXiv.2406.14464
Open Access
KI-basierte Sprachmodelle in der Lehre: Question-Generation-Modelle zur Messung des Lernfortschrittes von Studierenden
Woldai, Betiel ; Schacht, Sigurd; Kamath Barkur, Sudarshan (2024)
Neues Handbuch Hochschullehre - Sonderausgabe zur TURN23.
Open Access
Systematic Evaluation of Different Approaches on Embedding Search
Aperdannier, Roman; Köppel, Melanie; Unger, Tamina; Schacht, Sigurd; Kamath Barkur, Sudarshan (2024)
Aperdannier, Roman; Köppel, Melanie; Unger, Tamina; Schacht, Sigurd...
Future of Information and Communication Conference (FICC) 2024 2024.
DOI: 10.1007/978-3-031-53963-3_36
Peer Reviewed
Magenta: Metrics and Evaluation Framework for Generative Agents based on LLMs
Kamath Barkur, Sudarshan ; Schacht, Sigurd (2024)
In: Tareq Ahram, Waldemar Karwowski, Dario Russo and Giuseppe Di Bucchianico (eds) Intelligent Human Systems Integration (IHSI 2024): Integrating People and Intelligent Systems. AHFE (2024) International Conference. AHFE Open Access, vol 119. AHFE International, USA..
DOI: 10.54941/ahfe1004478
Open Access Peer Reviewed
PHOENIX: Open-Source Language Adaption for Direct Preference Optimization
Uhlig, Matthias; Schacht, Sigurd; Kamath Barkur, Sudarshan (2024)
Arxiv 2024.
DOI: 10.48550/arXiv.2401.10580
Open Access
The Social Media Hate Speech Barometer: Making of
Sauer, Sebastian; Piazza, Alexander; Schacht, Sigurd (2023)
Proceedings of the 5th International Conference of Business Meets Technology 2023, Valencia.
DOI: 10.4995/BMT2023.2023.16724
Open Access Peer Reviewed
A Qualitative Evaluation of an AI-Based Study Progress Forecast.
Kamath Barkur, Sudarshan ; Fersch, Mascha-Lea; Henne, Sophie ; Schacht, Sigurd; Woldai, Betiel (2023)
Kamath Barkur, Sudarshan ; Fersch, Mascha-Lea; Henne, Sophie ; Schacht, Sigurd...
In: Schlippe, T., Cheng, E.C.K., Wang, T. (eds) Artificial Intelligence in Education Technologies: New Development and Innovative Practices. AIET 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 190. Springer, Singapore .
DOI: 10.1007/978-981-99-7947-9_1
Peer Reviewed
Ontology Definition for University Knowledge Graph
Woldai, Betiel ; Kamath Barkur, Sudarshan ; Henne, Sophie ; Schacht, Sigurd; Schmid , Elena (2023)
Woldai, Betiel ; Kamath Barkur, Sudarshan ; Henne, Sophie ; Schacht, Sigurd...
The Barcelona Conference on Education 2023: Official Conference Proceedings.
DOI: 10.22492/issn.2435-9467.2023.69
Open Access
Finding the required information to succeed in the organisation of everyday study life is not always easy for a student. Ontologies are an instrument to define a domain by illustrating its concepts and thereby presenting knowledge in a structured way. In this paper, our aim is to design an ontology that is suitable for the higher education environment of a German university to build a Knowledge Graph for a conversational AI. As a research context, the Ansbach University of Applied Science is used. The paper is organised into five sections. After a brief introduction in Section 1, Section 2 reviews previous work of conducted ontologies within the higher education environment, whereas Section 3 outlines the methodology for developing the ontology and presents the final result. The development procedure is thereby partly based on the ontology framework provided by Stanford University (Noy & McGuinness, 2001). The presented ontology, which delivers possible classes for the development, and transferability to other universities will then be discussed in Section 4. Finally, the conclusion and approaches for future work with ensuring a constant up-to-dateness of the classes are given in Section 5.
Knowledge Science - Grundlagen. Methoden der Künstlichen Intelligenz für die Wissensextraktion aus Texten.
Lanquillon, Carsten; Schacht, Sigurd (2023)
Knowledge Science, 1. Auflage Springer Vieweg 2023 2023.
DOI: 10.1007/978-3-658-41689-8
Knowledge Science beschäftigt sich mit Konzepten, Methoden und Prozessen zur systematischen Erzeugung, Extraktion, Speicherung und Bereitstellung von Wissen zur Lösung von Problemen und lässt sich somit dem Wissensmanagement zuordnen. Kognitive Assistenten sorgen dafür, das richtige Wissen zur richtigen Zeit in der richtigen Art und Weise seinen Anwendern und Anwenderinnen bereitzustellen. Damit dies gelingen kann, kommen inzwischen zahlreiche Methoden der Künstlichen Intelligenz (KI) zur Unterstützung unterschiedlicher Aufgaben des Wissensmanagements zum Einsatz.
Knowledge Science - Fallstudien. Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird
Lanquillon, Carsten; Schacht, Sigurd (2023)
Springer Vieweg Wiesbaden.
DOI: 10.1007/978-3-658-41155-8
Trendermittlung mit der Unterstützung eines kognitiven Assistenten
Horbaschk, Nora; Schacht, Sigurd (2023)
Knowledge Science – Fallstudien: Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird, Springer Vieweg, Wiesbaden, S. 149-175.
DOI: 10.1007/978-3-658-41155-8_7
Kognitive Assistenzsysteme im Projektmanagement
Henne, Sophie ; Schacht, Sigurd (2023)
Knowledge Science - Fallstudien: Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird, Springer Vieweg, Wiesbaden, S. 177-191.
DOI: 10.1007/978-3-658-41155-8_8
Lernen wie ein Mensch: Konzept eines Assistenten mit Wissenserwerb durch Beobachtung, Instruktion und Interaktion
Dreßler, Daniel; Schacht, Sigurd; Lanquillon, Carsten (2023)
Knowledge Science – Fallstudien: Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird, Springer Vieweg, Wiesbaden, S. 69-107.
DOI: 10.1007/978-3-658-41155-8_5
Generative Agents to Support Students Learning Progress
Schacht, Sigurd; Kamath Barkur, Sudarshan ; Lanquillon, Carsten (2023)
In Proc.: 5th International Conference Business Meets Technology. Valencia, 13th-15th July 2023. 179-197. .
DOI: 10.4995/BMT2023.2023.16750
Open Access Peer Reviewed
Intro zu ChatGPT & Co - ein Experiment vom AN[ki]T "AI talks to AI"
Schacht, Sigurd; Piazza, Alexander (2023)
KI-Stammtisch des KI-Hub Bayern am Nürnberg DIGITAL FESTIVAL.
Knowledge-Grounded and Self-Extending NER
Kamath Barkur, Sudarshan ; Schacht, Sigurd; Lanquillon, Carsten (2023)
In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1836. .
DOI: 10.1007/978-3-031-36004-6_60
Peer Reviewed
The wave of digitization has begun. Organizations deal with huge amounts of data, such as logs, websites, and documents. A common way to make the information contained in these sources machine-accessible for automated processing is to first extract the information and then store it in a knowledge graph. A key task in this approach is to recognize entities. While common named entity recognition (NER) models work well for common entity types, they typically fail to recognize custom entities. Custom entity recognition requires data to be manually annotated and custom NER models to be trained. To efficiently extract the information, this paper proposes an innovative solution: Our Gazetteer approach uses a knowledge graph to create a coarse and fast NER component, reducing the need for manual annotation and saving human effort. Focusing on a university use case, our Gazetteer is integrated into a chatbot for entity recognition. In addition, data can be annotated using the Gazetteer and an NER model can be trained. Subsequently, the NER model can be used to recognize unseen custom entities, which are then added to the knowledge graph. This will improve the knowledge graph and make it self-extending.
PromptIE - Information Extraction with Prompt-Engineering and Large Language Models
Schacht, Sigurd; Kamath Barkur, Sudarshan ; Lanquillon, Carsten (2023)
In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1836. 1836.
DOI: 10.1007/978-3-031-36004-6_69
Peer Reviewed
Extracting triples of subjects, objects, and predicates from text to populate knowledge bases traditionally involves several intermediate steps such as co-reference resolution, named entity recognition, and relationship extraction. Treating triple extraction as translation task from source sentences to sets of triples, we present an end-to-end solution for information extraction that uses task prefixes to prompts a fine-tuned large language model to extract triples from text. Thus, the need for data labeling and training multiple models is reduced.
Prof. Dr. Sigurd Schacht
Hochschule Ansbach
Hochschule Ansbach - Fakultät Wirtschaft
Residenzstr. 8
91522 Ansbach
sigurd.schacht[at]hs-ansbach.de
ORCID iD: 0000-0002-1161-4724