用近似贝叶斯推断让大模型学会表达不确定，提升临床诊断可靠性。
Uncertainty Quantification of Large Language Models using Approximate Bayesian Computation
- 将大模型视为随机模拟器，用近似贝叶斯方法推断预测概率后验分布。
- 在两个临床数据集上，准确率提升46.9%，Brier分数降低74.4%。
- 适合需高可信度输出的医疗等安全关键场景，改善模型校准性。
尽管大型语言模型广泛应用，但在临床诊断等高风险领域常无法表达不确定性，影响可靠部署。现有基于模型logits和诱导概率的标准方法产生过度自信且校准差的估计。本文提出基于近似贝叶斯推断（ABC）的方法，将大模型视为随机模拟器，推断预测概率的后验分布。我们在两个临床相关基准上评估：合成口腔病变诊断数据集与公开的GretelAI症状到诊断数据集。相比标准基线，该方法在准确率上提升最高达46.9%，Brier分数降低74.4%，并显著改善校准性，如预期校准误差（ECE）和预测熵指标。
原文摘要 · Abstract (English)
Despite their widespread applications, Large Language Models (LLMs) often struggle to express uncertainty, posing a challenge for reliable deployment in high stakes and safety critical domains like clinical diagnostics. Existing standard baseline methods such as model logits and elicited probabilities produce overconfident and poorly calibrated estimates. In this work, we propose Approximate Bayesian Computation (ABC), a likelihood-free Bayesian inference, based approach that treats LLMs as a stochastic simulator to infer posterior distributions over predictive probabilities. We evaluate our ABC approach on two clinically relevant benchmarks: a synthetic oral lesion diagnosis dataset and the publicly available GretelAI symptom-to-diagnosis dataset. Compared to standard baselines, our approach improves accuracy by up to 46.9\%, reduces Brier scores by 74.4\%, and enhances calibration as measured by Expected Calibration Error (ECE) and predictive entropy.
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