Papers by Markus Frohmann
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization (2024.emnlp-main)
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| Challenge: | Existing methods for debiasing depend on attribute labels and target attributes. |
| Approach: | They propose a method that uses class-wise variance of embeddings to reduce the effects of debiasing on a downstream task. |
| Outcome: | The proposed method outperforms baselines that rely on attribute labels while maintaining performance on the target task. |
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation (2024.emnlp-main)
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| Challenge: | Sentence segmentation is a key task in many NLP systems, but no prior method has achieved all of the features needed to segment a text into sentences. |
| Approach: | They propose a new model that uses punctuation to enhance robustness and adaptability. |
| Outcome: | The proposed model outperforms baselines across 8 corpora across diverse domains and languages and is available under the MIT license. |
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale (2024.findings-acl)
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| Challenge: | Multi-task learning (MTL) has shown significant practical benefits when using language models . current two stage MTL introduces a substantial number of additional parameters . |
| Approach: | They propose a multi-task learning method that leverages existing knowledge for a target task. |
| Outcome: | The proposed method outperforms baselines on three benchmarks and two encoder LMs with a small number of transfer parameters. |
Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion (2025.findings-acl)
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| Challenge: | Existing methods for detecting AI-generated music are weak and vulnerable to audio perturbations. |
| Approach: | They propose a multimodal late-fusion pipeline that combines automatically transcribed sung lyrics and speech features capturing lyrics related information within the audio. |
| Outcome: | The proposed method outperforms existing detectors while being more robust to audio perturbations. |
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition (2024.findings-eacl)
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| Challenge: | Existing and new approaches to zero-shot knowledge composition are lacking in NLP. |
| Approach: | They propose a framework for zero-shot module composition that unifies existing and some novel variations for selecting, weighting, and combining parameter modules under a single unified notion. |
| Outcome: | The proposed framework enables a systematic unification of concepts and enables the first comprehensive benchmarking study of various zero-shot knowledge composition strategies. |