Demographic-conditioned fusion embeddings improve prediction of perspectivist social meaning interpretations by 5.9-6.5% relative macro PR-AUC over text-only baselines, with ablations confirming demographic signal.
The Importance of Modeling Social Factors of Language: Theory and Practice
5 Pith papers cite this work, alongside 117 external citations. Polarity classification is still indexing.
5
Pith papers citing it
117
external citations · OpenAlex
representative citing papers
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.
Socio-Contrastive Learning jointly learns socio-demographic representations and textual features via contrastive objectives to predict annotator perspectives more accurately than concatenation baselines.
Generative AI evaluation must shift from static benchmark scores to measuring sustained improvements in human capabilities within specific deployment contexts.
citing papers explorer
-
Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings
Demographic-conditioned fusion embeddings improve prediction of perspectivist social meaning interpretations by 5.9-6.5% relative macro PR-AUC over text-only baselines, with ablations confirming demographic signal.
-
Ethical and social risks of harm from Language Models
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
-
Quantifying and Predicting Disagreement in Graded Human Ratings
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.
-
Modeling Human Perspectives with Socio-Demographic Representations
Socio-Contrastive Learning jointly learns socio-demographic representations and textual features via contrastive objectives to predict annotator perspectives more accurately than concatenation baselines.
-
Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility
Generative AI evaluation must shift from static benchmark scores to measuring sustained improvements in human capabilities within specific deployment contexts.