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Maximilian Spliethöver

4 accepted papers

2025

Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection

NAACL 2025long

Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the success of techniques depends on various parameters, such as the task, language model, and context provided. Finding an eff…

2024

Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness

ACL 2024findings

Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-r…

2022

No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media

EMNLP 2022finding

News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on w…

2021

Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models

IJCAI 2021poster

Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable thes…