Hi there,

my name is Luca, and I’m a PhD student at the University of Bern. Currently, my research focuses on Natural Language Processing (NLP) and Black-Box Optimization (BBO). I am part of a four-year project funded by the Swiss National Science Foundation, which aims to detect and quantify the use of sustainable criteria in public procurement processes in Switzerland. Lately, I have been working on Information Retrieval, Legal Summarization, and the application of BBO methods to NLP problems.

If you are interested in my work, feel free to explore my publications, or contact me via email.

Latest publications

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Grouped bar chart showing two fine-tuned LLMs of the Qwen2.5 family and two frontier models evaluated on the SLDS test set across ROUGE, BLEU and LLM-as-a-Judge metrics.

Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland

Luca Rolshoven, Vishvaksenan Rasiah, Srinanda Brügger Bose, Sarah Hostettler, Lara Burkhalter, Matthias Stürmer, Joel Niklaus (2025)

Findings of the Association for Computational Linguistics: EMNLP 2025 — Suzhou, China

We present SLDS, a cross-lingual dataset of 20K Swiss court rulings with expert-authored headnotes in German, French, and Italian. Evaluation results suggest that factual accuracy matters more than reasoning for legal headnote generation.

Paper Code Dataset Models

Zero-Shot Award Criteria extraction via Large Language Models from German Procurement Data from Switzerland

Veton Matoshi, Luca Rolshoven, Matthias Stürmer (2024)

Proceedings of the 9th edition of the Swiss Text Analytics Conference — Chur, Switzerland

We show how a single zero-shot German LLM prompt can automatically extract and structure Award Criteria from Swiss calls for tenders. By jointly performing classification, named entity recognition, relation extraction, and formatting, our approach enables scalable monitoring of sustainability-related procurement practices. We evaluate on 167 annotated tenders and release both code and data publicly.

Paper Code

Illustration of the core architecture of DiBB with a head node sending and receieving the global mean, whereas several blockworkers evaluate their local candidate solutions as part of the global mean.

DiBB: Distributing Black-Box Optimization

Giuseppe Cuccu, Luca Rolshoven, Fabien Vorpe, Philippe Cudré-Mauroux, Tobias Glasmachers (2022)

Proceedings of the Genetic and Evolutionary Computation Conference — New York, NY, USA

We introduce DiBB, a framework for scalable black-box optimization that decomposes parameters into correlated blocks and optimizes them in parallel, preserving key properties of the underlying algorithms while enabling efficient distributed execution and large-scale applications.

Paper Code