Anum Afzal

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 ProjectsPublications (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]

Research Projects

Enterprise AI at SAP

SAP and TUM collaborate in strategic areas, where applied research can provide a positive impact to business and people. The portfolio of joint activities cover a broad set of areas as SAP‘s solutions is used in various aspects. "Enterprise AI" is a crucial part of that portfolio under which several research projects are being carried out. There is currently two on-going projects and one completed project.

Current Project:

AutoRAG - Leveraging Bayesian Optimization for Accelerating RAG Pipeline Optimization

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.

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