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Markus Frohmann

4 accepted papers

2025

Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion

ACL 2025finding

The rapid advancement of AI-based music generation tools is revolutionizing the music industry but also posing challenges to artists, copyright holders, and providers alike. This necessitates reliable methods for detecting such AI-generated content. However, existing detectors, relying on either aud…

2024

ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale

ACL 2024findings

Multi-task learning (MTL) has shown considerable practical benefits, particularly when using language models (LMs). While this is commonly achieved by learning tasks under a joint optimization procedure, some methods, such as AdapterFusion, divide the problem into two stages: (i) task learning, wher…

2024

Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation

EMNLP 2024main

Segmenting text into sentences plays an early and crucial role in many NLP systems. This is commonly achieved by using rule-based or statistical methods relying on lexical features such as punctuation. Although some recent works no longer exclusively rely on punctuation, we find that no prior method…

2024

Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization

EMNLP 2024main

Language models frequently inherit societal biases from their training data. Numerous techniques have been proposed to mitigate these biases during both the pre-training and fine-tuning stages. However, fine-tuning a pre-trained debiased language model on a downstream task can reintroduce biases int…