Go to ACL ARR 2024 December homepageGeneralised Probabilistic Modelling and Uncertainty Estimation in Comparative LLM-as-a-judge
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, allowing for 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 enhances 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. 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.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: LLM-as-a-judge, comparative, uncertainty, ranking
Contribution Types: Approaches low compute settings-efficiency
Languages Studied: English
Submission Number: 1092
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