Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models explores A post-hoc calibration method for large language models that improves the reliability of uncertainty estimation by aligning scores with factual correctness.. Commercial viability score: 8/10 in LLM Reliability.
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