Papers with human disagreement

2 papers
Thesis Proposal: Toward a Human-Centered and Perspective-Aware Framework for Reproducible ML Evaluation and AI Alignment (2026.acl-srw)

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Challenge: Disagreement arises from subjective human opinion and can vary with one’s identity, beliefs, and social environment.
Approach: They propose a human-centered framework for reproducible ML evaluation and AI alignment that takes disagreement into account when building human-centric AI systems.
Outcome: The proposed framework is based on a human-centered and perspective-aware framework for reproducible ML evaluation and AI alignment.
Seeing All Sides: Multi-Perspective In-Context Learning for Subjective NLP (2026.findings-eacl)

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Challenge: Modern language models excel at factual reasoning but struggle with value diversity, authors say . task-sensitive tasks such as hate speech expose this limitation . human disagreement captures the diversity of plausible human perspectives, authors argue .
Approach: They evaluate four large language models with human disagreements on five datasets . they find multi-perspective in-context learning outperforms standard prompting .
Outcome: The proposed approach outperforms standard prompting on English labels while disaggregated soft predictions better align with human judgments in Arabic and Italian datasets.

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