arXiv:2606.07123v1 Announce Type: new Abstract: Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP systems collapse this variation into a single ground-truth label, ignoring the diversity of interpretations. In this work, we model social dimensions along a perspectivist spectrum, capturing how interpretations vary across demographic groups on a dataset consisting of 28k human annotations. We benchmark multiple modeling paradigms, including zero-shot, few-shot, and fine-tuned approaches, and pro
The increasing sophistication and widespread deployment of AI necessitate a deeper understanding of how social meaning is interpreted across diverse demographics, moving beyond monolithic 'ground truths' in language models.
Strategic readers should care as the ability to model perspectivist social meaning is crucial for building more robust, equitable, and context-aware AI systems, impacting areas from public discourse analysis to personalized information delivery.
AI systems can potentially move beyond a single, generalized understanding of social meaning, instead incorporating and accounting for the diverse interpretations based on demographic and ideological positions.
- · AI ethicists
- · Social scientists
- · Companies deploying AI in diverse markets
- · NLP researchers
- · Developers relying on 'one-size-fits-all' AI models
- · Systems that perpetuate biases due to lack of diverse interpretation
AI models will gain the capacity to better understand and represent the nuanced social meanings embedded in human language, accounting for different demographics.
This improved understanding could lead to the development of AI systems that are more sensitive to cultural and demographic differences, reducing unintended biases and improving user interaction in diverse settings.
The broader adoption of perspectivist AI could fundamentally alter how information is analyzed and disseminated, potentially fostering more inclusive public discourse and personalized, contextually relevant information delivery across global populations.
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