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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cs.CL 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
ORPHEAS, a Greek-English embedding model created with knowledge graph fine-tuning, outperforms state-of-the-art multilingual models on monolingual and cross-lingual retrieval benchmarks.
Continual pre-training on a German medical corpus lets 7B models close much of the performance gap with 24B general models on medical benchmarks, though merging introduces some language mixing and verbosity.
Multilingual pooling for quality classifiers outperforms monolingual baselines in rank stability and accuracy for LLM pretraining data selection across high- and low-resource languages.
citing papers explorer
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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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ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generation
ORPHEAS, a Greek-English embedding model created with knowledge graph fine-tuning, outperforms state-of-the-art multilingual models on monolingual and cross-lingual retrieval benchmarks.
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Can Continual Pre-training Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain?
Continual pre-training on a German medical corpus lets 7B models close much of the performance gap with 24B general models on medical benchmarks, though merging introduces some language mixing and verbosity.
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Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection
Multilingual pooling for quality classifiers outperforms monolingual baselines in rank stability and accuracy for LLM pretraining data selection across high- and low-resource languages.