CSR improves LLM calibration by combining binary correctness rewards with a semantic calibration reward that promotes agreement on correct rollouts and discourages spurious consistency on incorrect ones, outperforming verbalized confidence baselines on ECE and AUROC across in- and out-of-domain QA任务
In these cases, it is also okay to have only a small number of uncertainties and then explicitly say that you are unable to spot more uncertainties
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Calibrating LLMs with Semantic-level Reward
CSR improves LLM calibration by combining binary correctness rewards with a semantic calibration reward that promotes agreement on correct rollouts and discourages spurious consistency on incorrect ones, outperforming verbalized confidence baselines on ECE and AUROC across in- and out-of-domain QA任务