Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge

• URL: http://arxiv.org/abs/2505.15240v1
• Date: Wed, 21 May 2025 08:16:18 GMT
• Title: Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge
• Authors: Yassir Fathullah, Mark J. F. Gales,
• Abstract summary: We show that existing Product-of-Experts methods are specific cases of a broader framework, enabling diverse modelling options.<n>We propose improved uncertainty estimates for individual comparisons, enabling more efficient selection and achieving strong performance with fewer evaluations.
• Score: 37.84914870036184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores generalised probabilistic modelling and uncertainty estimation in comparative LLM-as-a-judge frameworks. We show that existing Product-of-Experts methods are specific cases of a broader framework, enabling diverse modelling options. Furthermore, we propose improved uncertainty estimates for individual comparisons, enabling more efficient selection and achieving strong performance with fewer evaluations. We also introduce a method for estimating overall ranking uncertainty. Finally, we demonstrate that combining absolute and comparative scoring improves performance. Experiments show that the specific expert model has a limited impact on final rankings but our proposed uncertainty estimates, especially the probability of reordering, significantly improve the efficiency of systems reducing the number of needed comparisons by ~50%. Furthermore, ranking-level uncertainty metrics can be used to identify low-performing predictions, where the nature of the probabilistic model has a notable impact on the quality of the overall uncertainty.

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