Feb. 9, 2024, 5:47 a.m. | Tilman Beck Hendrik Schuff Anne Lauscher Iryna Gurevych

cs.CL updates on arXiv.org arxiv.org

Annotators' sociodemographic backgrounds (i.e., the individual compositions of their gender, age, educational background, etc.) have a strong impact on their decisions when working on subjective NLP tasks, such as toxic language detection. Often, heterogeneous backgrounds result in high disagreements. To model this variation, recent work has explored sociodemographic prompting, a technique, which steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. However, the available NLP literature disagrees on the efficacy of this technique …

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