Estonian subjectivity dataset created, LLM scoring tested

Researchers have developed a new Estonian-language dataset for document-level subjectivity analysis, comprising 1,000 texts rated on a scale from 0 to 100. Initial experiments using this dataset showed moderate inter-annotator agreement among human raters, prompting a re-annotation of divergent scores. An experiment using GPT-5 for automatic subjectivity scoring indicated feasibility but highlighted differences from human annotations, suggesting LLM-based scoring is not a direct substitute for human judgment. AI

IMPACT Provides a new resource for evaluating LLM understanding of subjective content in Estonian.

RANK_REASON The cluster contains an academic paper detailing the creation of a new dataset and an initial experiment. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Estonian
• GPT-5
• Karl Gustav Gailit

• paper
• other

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