Tom Völker

Chair of Data Science (Informatik X)
University of Würzburg
Campus Hubland Nord
Emil-Fischer-Straße 50
97074 Würzburg
Germany

Email: voelker[at]informatik.uni-wuerzburg.de
Phone: (+49 931) 31 - 83034
Office: Room 50.03.019 (Institutsgebäude Künstliche Intelligenz)

Research Interests & Projects

I joined the Data Science Chair in 2026 after studying Computer Science and Mathematics Education at the University of Würzburg. My research focuses on Natural Language Processing, particularly German language models, cross-lingual methods, and model architectures. I am involved in our project soofi, aiming to build sovereign open foundation models, and a co-maintainer of BibSonomy.

Selected Publications

2025[ to top ]

BARTABSA++: Revisiting BARTABSA with Decoder LLMs. Pfister, Jan; Völker, Tom; Vlasjuk, Anton; Hotho, Andreas. In Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025), H. Fei, K. Tu, Y. Zhang, X. Hu, W. Han, Z. Jia, Z. Zheng, Y. Cao, M. Zhang, W. Lu, N. Siddharth, L. Ovrelid, N. Xue, Y. Zhang (eds.), pp. 115–128. Association for Computational Linguistics, Vienna, Austria, 2025.
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We revisit the BARTABSA framework for aspect-based sentiment analysis with modern decoder LLMs to assess the importance of explicit structure modeling today. Our updated implementation - BARTABSA++ - features architectural enhancements that boost performance and training stability.Systematic testing with various encoder-decoder architectures shows that BARTABSA++ with BART-Large achieves state-of-the-art results, even surpassing a finetuned GPT-4o model.Our analysis indicates the encoder's representational quality is vital, while the decoder's role is minimal, explaining the limited benefits of scaling decoder-only LLMs for this task. These findings underscore the complementary roles of explicit structured modeling and large language models, indicating structured approaches remain competitive for tasks requiring precise relational information extraction.

@inproceedings{pfister-etal-2025-bartabsa, abstract = {We revisit the BARTABSA framework for aspect-based sentiment analysis with modern decoder LLMs to assess the importance of explicit structure modeling today. Our updated implementation - BARTABSA++ - features architectural enhancements that boost performance and training stability.Systematic testing with various encoder-decoder architectures shows that BARTABSA++ with BART-Large achieves state-of-the-art results, even surpassing a finetuned GPT-4o model.Our analysis indicates the encoder{'}s representational quality is vital, while the decoder{'}s role is minimal, explaining the limited benefits of scaling decoder-only LLMs for this task. These findings underscore the complementary roles of explicit structured modeling and large language models, indicating structured approaches remain competitive for tasks requiring precise relational information extraction.}, address = {Vienna, Austria}, author = {Pfister, Jan and V{\"o}lker, Tom and Vlasjuk, Anton and Hotho, Andreas}, booktitle = {Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)}, editor = {Fei, Hao and Tu, Kewei and Zhang, Yuhui and Hu, Xiang and Han, Wenjuan and Jia, Zixia and Zheng, Zilong and Cao, Yixin and Zhang, Meishan and Lu, Wei and Siddharth, N. and {\O}vrelid, Lilja and Xue, Nianwen and Zhang, Yue}, keywords = {author:pfister}, month = {08}, pages = {115–128}, publisher = {Association for Computational Linguistics}, title = {{BARTABSA}++: Revisiting {BARTABSA} with Decoder {LLM}s}, year = 2025 }
SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation. Völker, Tom; Pfister, Jan; Hotho, Andreas. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), S. Rosenthal, A. Rosá, D. Ghosh, M. Zampieri (eds.), pp. 852–864. Association for Computational Linguistics, Vienna, Austria, 2025.
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Entity-aware machine translation faces significant challenges when translating culturally-adapted named entities that require knowledge beyond the source text.We present SALT (SQL-based Approach for LLM-Free Entity-Aware-Translation), a parameter-efficient system for the SemEval-2025 Task 2.Our approach combines SQL-based entity retrieval with constrained neural translation via logit biasing and explicit entity annotations.Despite its simplicity, it achieves state-of-the-art performance (First Place) among approaches not using gold-standard data, while requiring far less computation than LLM-based methods.Our ablation studies show simple SQL-based retrieval rivals complex neural models, and strategic model refinement outperforms increased model complexity.SALT offers an alternative to resource-intensive LLM-based approaches, achieving comparable results with only a fraction of the parameters.

@inproceedings{volker-etal-2025-salt, abstract = {Entity-aware machine translation faces significant challenges when translating culturally-adapted named entities that require knowledge beyond the source text.We present SALT (SQL-based Approach for LLM-Free Entity-Aware-Translation), a parameter-efficient system for the SemEval-2025 Task 2.Our approach combines SQL-based entity retrieval with constrained neural translation via logit biasing and explicit entity annotations.Despite its simplicity, it achieves state-of-the-art performance (First Place) among approaches not using gold-standard data, while requiring far less computation than LLM-based methods.Our ablation studies show simple SQL-based retrieval rivals complex neural models, and strategic model refinement outperforms increased model complexity.SALT offers an alternative to resource-intensive LLM-based approaches, achieving comparable results with only a fraction of the parameters.}, address = {Vienna, Austria}, author = {Völker, Tom and Pfister, Jan and Hotho, Andreas}, booktitle = {Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)}, editor = {Rosenthal, Sara and Ros{\'a}, Aiala and Ghosh, Debanjan and Zampieri, Marcos}, keywords = {author:pfister}, month = {07}, pages = {852–864}, publisher = {Association for Computational Linguistics}, title = {{SALT} at {S}em{E}val-2025 Task 2: A {SQL}-based Approach for {LLM}-Free Entity-Aware-Translation}, year = 2025 }

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