In this work, we introduce the Learnable Response Scoring Function (LARS) for
Uncertainty Estimation (UE) in generative Large Language Models (LLMs). Current
scoring functions for probability-based UE, such as length-normalized scoring
and semantic contribution-based weighting, are designed to solve specific
aspects of the problem but exhibit limitations, including the inability to
handle biased probabilities and under-performance in low-resource languages
like Turkish. To address these issues, we propose LARS, a scoring function that
leverages supervised data to capture complex dependencies between tokens and
probabilities, thereby producing more reliable and calibrated response scores
in computing the uncertainty of generations. Our extensive experiments across
multiple datasets show that LARS substantially outperforms existing scoring
functions considering various probability-based UE methods.

本研究引入了可学习的响应评分函数（LARS）用于生成式大型语言模型中的不确定性估计（UE）。我们提出了 LARS，它利用监督数据来捕捉令牌和概率之间的复杂依赖关系，从而在计算生成物的不确定性时产生更可靠和校准的响应评分。我们在多个数据集上进行了广泛的实验证明，LARS 在各种基于概率的 UE 方法中显著优于现有的评分函数。