Papers by Markus Schedl
Parameter-efficient Modularised Bias Mitigation via AdapterFusion (2023.eacl-main)
Copied to clipboard
Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz
| Challenge: | Large pre-trained language models contain societal biases and carry along these biase . Current approaches to mitigate these bias impose debiasing by updating model parameters, effectively transferring model to irreversible debiased state. |
| Approach: | They propose to develop stand-alone debiasing functionalities separate from the model, which can be integrated into the model on-demand while keeping the core model untouched. |
| Outcome: | The proposed approach improves or maintains effectiveness of bias mitigation, avoids catastrophic forgetting in a multi-attribute scenario, and maintains on-par task performance while granting parameter-efficiency and easy switching between the original and debiased models. |
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters (2024.eacl-long)
Copied to clipboard
| Challenge: | a recent study focused on learning separate modules for on-demand debiasing. |
| Approach: | They propose a modular debiasing module with a controllable gate adapter . they demonstrate that the module can reduce the bias of search results . |
| Outcome: | The proposed module can reduce biases on three classification tasks with four protected attributes while maintaining higher task performance. |
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization (2024.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Cognitive Effects and Biases in Large Language Models (2026.eacl-tutorials)
Copied to clipboard
| Challenge: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Approach: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Outcome: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that pre-trained language models can be used to mitigate societal biases and stereotypes. |
| Approach: | They propose a modular bias mitigation approach that integrates debiasing modules into the core model on-demand at inference time. |
| Outcome: | The proposed approach improves on-par with baseline finetuning on gender, race, and age protected attributes on three classification tasks with gender, age, and race as protected attributes. |
Characterizing Positional Bias in Large Language Models: A Multi-Model Evaluation of Prompt Order Effects (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models can be influenced by various forms of biases, says a new study . positional bias affects how LLMs interpret and weigh information, the authors say . |
| Approach: | a new study examines the impact of positional bias on large language models . positional biased models prioritize items based on their position rather than content or quality . |
| Outcome: | a new study shows that LLMs prioritize items based on their position rather than content or quality . the positional bias affects how LLM interpret and weigh information, the authors say . |
Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion (2025.findings-acl)
Copied to clipboard
| 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. |