Context and retrieved moral knowledge improve sentence-level Schwartz value detection more consistently than model scaling, with early-fusion RAG outperforming other variants in matched comparisons.
Mining the uncertainty patterns of humans and models in the annotation of moral foundations and human values
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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LLMs show split alignment with human hate speech annotations (strong on explicit attributes, inverted on evaluative ones), and attribute-based ridge regression reconstructs continuous scores with R² up to 0.71.
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
citing papers explorer
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More Context, Larger Models, or Moral Knowledge? A Systematic Study of Schwartz Value Detection in Political Texts
Context and retrieved moral knowledge improve sentence-level Schwartz value detection more consistently than model scaling, with early-fusion RAG outperforming other variants in matched comparisons.
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Attribute-Based Diagnosis of LLM Alignment with Hate Speech Annotations
LLMs show split alignment with human hate speech annotations (strong on explicit attributes, inverted on evaluative ones), and attribute-based ridge regression reconstructs continuous scores with R² up to 0.71.
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The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.