Small open-source LLMs achieve competitive system-level correlations with human judgments in machine translation quality estimation, outperforming traditional neural metrics and fine-tuned models via single-pass multi-output prompting.
and Kanojia, Diptesh and C
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cs.CL 3years
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UNVERDICTED 3representative citing papers
Large-scale benchmarks of multilingual embeddings and QE models show no universal performer; direction-aware routing and calibration recommended for parallel data assessment.
ROC analysis is proposed for evaluating translation quality estimation systems, claimed to match existing methods while providing actionable business insights.
citing papers explorer
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CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
Small open-source LLMs achieve competitive system-level correlations with human judgments in machine translation quality estimation, outperforming traditional neural metrics and fine-tuned models via single-pass multi-output prompting.
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Model-Based Quality Assessment for Massively Multilingual Parallel Data
Large-scale benchmarks of multilingual embeddings and QE models show no universal performer; direction-aware routing and calibration recommended for parallel data assessment.
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ROC Analysis for Evaluating Translation Quality Estimation Systems
ROC analysis is proposed for evaluating translation quality estimation systems, claimed to match existing methods while providing actionable business insights.