Papers by Bettina Berendt
Computational Ad Hominem Detection (P19-2)
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| Challenge: | ad hominem attacks are introduced in debates as an easy win, but their impact on argumentation is limited . a machine learning approach to detect the personal attack is insufficient, we show . |
| Approach: | They propose a machine learning approach that detects ad hominem attacks using social media data . they propose TF-IDF approaches that are insufficient to detect the personal attack . |
| Outcome: | The proposed method has a recall of 80% for a social media data source. |
RobBERT: a Dutch RoBERTa-based Language Model (2020.findings-emnlp)
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| Challenge: | Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks. |
| Approach: | They used a robustly optimized BERT approach to train a Dutch language model called RobBERT. |
| Outcome: | The proposed model outperforms models trained on a single language on dozens of tasks and is available for further downstream NLP applications. |
Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models (2022.naacl-main)
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| Challenge: | An increasing awareness of biased patterns in natural language processing resources such as BERT has motivated many metrics to quantify ‘bias’ and ‘fairness’. |
| Approach: | They combine literature survey, correlation analysis and empirical evaluations to evaluate compatibility of fairness metrics for pre-trained language models and their downstream tasks. |
| Outcome: | The proposed measures are not compatible with each other and highly depend on (i) templates, (ii) attribute and target seeds and (iv) the choice of embeddings. |
How Far Can It Go? On Intrinsic Gender Bias Mitigation for Text Classification (2023.eacl-main)
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| Challenge: | a growing interest in exploring how gender bias pertains in contextualized language models has been generated . intrinsic mitigation strategies and bias metrics have been proposed to mitigate gender bias in contextualised language models . |
| Approach: | They propose to use different intrinsic bias mitigation strategies to mitigate gender bias in contextualized language models. |
| Outcome: | The proposed probe shows that some mitigation techniques can hide gender bias . the probe also shows that not all mitigation techniques fool extrinsic bias despite their use . |
Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs (2026.findings-acl)
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| Challenge: | Large Language Models exhibit systematic biases across demographic groups. |
| Approach: | They propose to use auditing as uncertainty estimation over a fairness metric . they propose to introduce the Bounded Active Fairness Auditor for query-efficient auditing . |
| Outcome: | The proposed auditing tool reduces query access costs and improves performance over time. |