LLMs default to responses more similar to opinions from the USA and some European and South American countries; prompting for a country shifts alignment but can introduce stereotypes, while translation does not reliably match language speakers.
CHI Conference on Human Factors in Computing Systems , publisher =
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Agreement-based clustering of annotators improves performance on subjective NLP tasks by capturing diverse perspectives better than majority voting or per-annotator modeling.
The paper formalizes three types of pluralistic AI models and three benchmark classes, arguing that current alignment techniques may reduce rather than increase distributional pluralism.
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.
A workshop proposal to reflect on HCI's core identity and the importance of human elements in the era of generative AI.
citing papers explorer
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Towards Measuring the Representation of Subjective Global Opinions in Language Models
LLMs default to responses more similar to opinions from the USA and some European and South American countries; prompting for a country shifts alignment but can introduce stereotypes, while translation does not reliably match language speakers.
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Beyond Majority Voting: Agreement-Based Clustering to Model Annotator Perspectives in Subjective NLP Tasks
Agreement-based clustering of annotators improves performance on subjective NLP tasks by capturing diverse perspectives better than majority voting or per-annotator modeling.
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A Roadmap to Pluralistic Alignment
The paper formalizes three types of pluralistic AI models and three benchmark classes, arguing that current alignment techniques may reduce rather than increase distributional pluralism.
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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.
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What is (H)CI: Why Does the "Human'' Matter?
A workshop proposal to reflect on HCI's core identity and the importance of human elements in the era of generative AI.