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Adaptive sampling can cut human evaluation costs while boosting the accuracy of model rankings in NLP tasks.
Evaluators using cESA can achieve more reliable translation quality assessments while cutting annotation time by leveraging shared context across multiple outputs.
New benchmarks reveal that even advanced LLMs struggle with cultural value alignment, but targeted fine-tuning using Sri Lankan values can significantly enhance their performance.
Command A shows how to build an enterprise-grade LLM that balances performance, efficiency, and multilingual capabilities using decentralized training and model merging.
