Anum Afzal

Last modified by Anum Afzal Nov 9

staff sebis computer vision nlp deep learning

Faculty of Informatics
Chair of Informatics 19
Software Engineering for Business Information Systems (sebis)

Technical University of Munich
Boltzmannstraße 3
85748 Garching, Germany

anum.afzal [at] tum.de

Room FMI 01.12.055

Office hours: by appointment

I do not supervise industry thesis.

Open Positions:

I have no open thesis topics

Curriculum Vitae

Anum is Ph.D. candidate at the Technical University of Munich with a focus on topics such as Efficiency and Domain Adaptation of Large Language Models. She has been a researcher at the chair for Software Engineering of Business Information Systems (sebis) at the Technical University of Munich (TUM) since September 2021.

She is also a part of the Industry on Campus initiative and support SAP @ TUM Collaboration Lab. Apart from the theoretical research on domain-specific text summarization, she works with SAP on a research project on application of LLMs in a business context. She also collaborates with Holtzbrinck Publishing Group on a Domain-specifc Text Summarization project as a part of the Software Campus initiative.

She holds a master's degree in Computer Science from TUM and wrote her master thesis about Topic Modeling for Employee Objectives using Word Embeddings with Merck Group. She has also worked as a research assistant at chair of Information systems at TUM and also did a student internship at Munich Re during her Master's.

Research Interests

Text Summarization
Domain-Adaptation
Efficient Transformers, Parameter-efficient fine-tuning
Conversational AI, chatbots
Natural Language Generation Evaluation

Research Projects

Applications of Text Generation through Semi-supervised learning and beyond

The project explores semi-supervised learning frameworks with applications of text generation using deep learning models. This project addresses several research questions, such as the development of an approach for automated labeling of data generated via chatbot interactions, the integration of user feedback for an enhanced learning experience for the chatbot, and the improvement of chatbot responses where only a limited dataset is available for training.

ATESD: Abstractive Text Summarization for Domain-Specific Documents

Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as domain-specific abstractive text summarization. This paper identifies three of those limitations as research problems in the context of abstractive text summarization: 1) Quadratic complexity of transformer-based models with respect to the input text length; 2) Model Hallucination, which is a model's ability to generate factually incorrect text; and 3) Domain Shift, which happens when the distribution of the model's training and test corpus is not the same. Along with a discussion of the open research questions, this project addresses these research gaps in the context of Abstractive Text Summariztion.

Teaching (in reverse chronological order)

Term	Level	Title	Type	Role
SS 24	Master	Natural Language Processing - Methods and Applications	Seminar	Organizer
SS 24	Master	SEBA Lab Course (NLP)	Lab Course	Advisor
SS 24	Master	Software Engineering for Business Applications (SEBA Master)	Lab Course	Advisor
SS 23	Master	Natural Language Processing - Methods and Applications	Seminar	Organizer
SS 23	Master	SEBA Lab Course (NLP)	Lab Course	Advisor
WS 22/23	Master	SEBA Lab Course	Lab Course	Advisor
SS 22	Master	Natural Language Processing - Methods and Applications	Seminar	Organizer
SS 22	Master / Bachelor	Conversational AI workshop	Certificate Course	Organizer
SS 22	Master	Software Engineering for Business Applications (SEBA Master)	Lab Course	Advisor
WS 21/22	Master	SEBA Lab Course	Lab Course	Advisor

Publications (in reverse chronological order)

2024
[Link]	Afzal, Anum; Chalumattu, Ribin; Matthes, Florian; Espuny, Laura Mascarell; AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization. 2024. arXiv preprint arXiv: 2407.11591
[Link]	Afzal, Anum & Fani, Rajna & Kowsik, Alexander & Matthes, Florian. Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human-in-the-Loop. Proceedings of the Workshops on Data Science with Human in the Loop in North American Chapter of the Association for Computational Linguistics (NAACL 2024), Mexico City, Mexico [Best Paper Award]
[Link]	Afzal, Anum & Xiang, Tao & Matthes, Florian. A Semi-Automatic light-weight Approach towards Data Generation for a Domain-Specific FAQ chatbot using Human-in-the-Loop. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence (ICAART 2024), Rome, Italy. SCITEPRESS - Science and Technology Publications.
2023
[Link]	Schneider, P.; Afzal, A.; Vladika, J.; Braun, D.; Matthes, F. Investigating Conversational Search Behavior For Domain Exploration. In European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland. Springer.
Link]	Afzal, A.; Vladika, J.; Braun, D.; Matthes, F. Challenges in Domain-Specific Abstractive Summarization and How to Overcome Them. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence (ICAART 2023), Lisbon, Portugal. SCITEPRESS - Science and Technology Publications. [Best Paper Runner-up]