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Proposes CSR, a framework that calibrates LLMs directly in semantic space using a novel semantic calibration reward, reducing ECE by up to 40% and improving AUROC by up to 31% over verbalized-confidence baselines across multiple datasets.
This paper proposes using reinforcement learning with semantic rewards (via GRPO) to expand LLMs to low-resource languages without the typical alignment tax of catastrophic forgetting, showing improved semantic quality and transferability over supervised fine-tuning.