Papers by Pieter Delobelle
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 . |
Metrics for What, Metrics for Whom: Assessing Actionability of Bias Evaluation Metrics in NLP (2024.emnlp-main)
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| Challenge: | a measure’s intended use and reliability assessment are often unclear or entirely absent from the literature examining bias measures in natural language processing. |
| Approach: | They propose a set of desiderata to assess the degree to which a measure’s results enable informed action and a review of 146 papers proposing bias measures in NLP. |
| Outcome: | The proposed desiderata are based on 146 papers proposing bias measures in natural language processing (NLP) . they show that key elements of actionability, including a measure’s intended use and reliability assessment, are often unclear or entirely absent. |
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. |
BPE-knockout: Pruning Pre-existing BPE Tokenisers with Backwards-compatible Morphological Semi-supervision (2024.naacl-long)
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| Challenge: | Subword tokenisation is a mainstay in natural language processing (NLP) it allows the representation of an infinite space of text with a finite set of units. |
| Approach: | They propose to use byte-pair encoding to represent an infinite space of text with a finite set of units by removing subwords from the BPE vocabulary without impeding further use of merges that relied on them. |
| Outcome: | The proposed method improves token-based tasks in English, Dutch and German while saving training costs. |
SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models (2025.naacl-long)
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Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle, Manan Dey, Sil Hamilton, Timm Dill, Jad Doughman, Ritam Dutt, Avijit Ghosh, Jessica Zosa Forde, Carolin Holtermann, Lucie-Aimée Kaffee, Tanmay Laud, Anne Lauscher, Roberto L Lopez-Davila, Maraim Masoud, Nikita Nangia, Anaelia Ovalle, Giada Pistilli, Dragomir Radev, Beatrice Savoldi, Vipul Raheja, Jeremy Qin, Esther Ploeger, Arjun Subramonian, Kaustubh Dhole, Kaiser Sun, Amirbek Djanibekov, Jonibek Mansurov, Kayo Yin, Emilio Villa Cueva, Sagnik Mukherjee, Jerry Huang, Xudong Shen, Jay Gala, Hamdan Al-Ali, null Tair Djanibekov, Nurdaulet Mukhituly, Shangrui Nie, Shanya Sharma, Karolina Stanczak, Eliza Szczechla, Tiago Timponi Torrent, Deepak Tunuguntla, Marcelo Viridiano, Oskar Van Der Wal, Adina Yakefu, Aurélie Névéol, Mike Zhang, Sydney Zink, Zeerak Talat
| Challenge: | Large Language Models reproduce and exacerbate social biases present in training data, and resources to quantify this issue are limited. |
| Approach: | They propose a multilingual parallel dataset to examine culturally-specific stereotypes that may be learned by LLMs. |
| Outcome: | The proposed dataset includes stereotypes from 20 regions around the world and 16 languages, spanning multiple identity categories subject to discrimination worldwide. |