Towards reliable truth-aligned uncertainty estimation in large language models
In brief
- Current uncertainty metrics fail to predict the factuality of LLM responses.
- Truth AnChoring (TAC) recalibrates uncertainty scores to align with truth.
- The method works with limited data and improves model reliability.
Current uncertainty estimation methods in large language models often fail to predict the factuality of responses. The authors propose a post-hoc calibration approach, Truth AnChoring (TAC), to align these estimates with truth. This method improves model reliability without requiring large datasets.
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