Papers by Markus Müller
Wanda++: Pruning Large Language Models via Regional Gradients (2025.findings-acl)
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Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan, Goeric Huybrechts, Markus Müller, Jonas M. Kübler, Rupak Vignesh Swaminathan, Athanasios Mouchtaris, Sravan Babu Bodapati, Nathan Susanj, Zheng Zhang, Jack FitzGerald, Abhishek Kumar
| Challenge: | Existing pruning methods suffer from accuracy degradation without full-model sparsity-aware fine-tuning. |
| Approach: | They propose a pruning framework that uses decoder-block-level regional gradients to improve pruning accuracy. |
| Outcome: | The proposed pruning framework outperforms the state-of-the-art pruning frameworks by utilizing decoder-block-level regional gradients. |
PlanRAG-Audio: Planning and Retrieval Augmented Generation for Long-form Audio Understanding (2026.findings-acl)
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Masao Someki, Chien-yu Huang, Siddhant Arora, Samuele Cornell, Markus Müller, Nathan Susanj, Rupak Vignesh Swaminathan, Grant Strimel, Jing Liu, Shinji Watanabe
| Challenge: | Long-form audio understanding poses significant challenges due to the extreme length of audio sequences and the need to reason over heterogeneous acoustic cues distributed over time. |
| Approach: | They propose a retrieval-augmented generation framework for scalable long-form audio understanding . planRAG-Audio explicitly plans which modalities and temporal spans are required for a given query . |
| Outcome: | Experiments show that planRAG-Audio reduces the length of inputs for long-form audio models . the proposed framework can efficiently reason over long-term speech data . |
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning (C18-2)
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Florian Dessloch, Thanh-Le Ha, Markus Müller, Jan Niehues, Thai-Son Nguyen, Ngoc-Quan Pham, Elizabeth Salesky, Matthias Sperber, Sebastian Stüker, Thomas Zenkel, Alexander Waibel
| Challenge: | In today's globalized world, communication is difficult and often the language barrier still prevents communication. |
| Approach: | They have developed a low-latency translation system that is adapted to lectures and covers several language pairs. |
| Outcome: | The proposed system improves performance but also covers several European languages. |
BULBasaa: A Bilingual Basaa-French Speech Corpus for the Evaluation of Language Documentation Tools (L18-1)
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Fatima Hamlaoui, Emmanuel-Moselly Makasso, Markus Müller, Jonas Engelmann, Gilles Adda, Alex Waibel, Sebastian Stüker
| Challenge: | Approximately 50 hours of Bàsàá speech were collected and then carefully re-spoken and orally translated into French . |
| Approach: | They propose to provide an automatic phonetic transcription using a set of derived phone-like units. |
| Outcome: | The proposed method provides an automatic phonetic transcription using a set of derived phone-like units. |