Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge (2505.15240v1)

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.

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

This paper, titled "Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge," investigates advanced probabilistic modelling techniques within the domain of LLMs employed as evaluative judges. The research focuses on expanding the repertoire of probabilistic models available for comparative judgement tasks, introducing robust uncertainty estimation methods, and enhancing the computational efficiency of scoring systems used to evaluate the quality of natural language generation.

Key Contributions

The paper makes several noteworthy contributions in the area of comparative assessment using LLMs:

1. Generalised Probabilistic Modelling Framework: The authors present a generalized framework that encapsulates existing Product-of-Experts methodologies as specific instances. This framework allows for the incorporation of diverse modelling options by leveraging different distributional assumptions and functional transformations, thereby providing flexibility and enhancing the adaptability of scoring systems.
2. Improved Uncertainty Estimates: A significant portion of the research is devoted to improving uncertainty estimations in comparative scoring. The proposed methods for individual comparisons provide refined estimates which facilitate more efficient selection processes. Importantly, these enhancements achieve comparable performance with a reduced number of evaluations, highlighting the efficacy of the proposed approach.
3. Estimation of Ranking Uncertainty: Beyond individual comparisons, the paper introduces methodologies for estimating uncertainty in overall rankings. This is particularly crucial for identifying predictions with low performance and understanding the implications of probabilistic modelling on the reliability of rankings.
4. Combination of Absolute and Comparative Scoring: The research demonstrates that blending absolute scoring with comparative scoring can yield improved results. This hybrid approach is analytically shown to complement each scoring strategy's strengths and mitigate their weaknesses, offering a more integrated solution.

Experimental Findings

The paper includes comprehensive experiments that substantiate the authors' claims. It is shown that the impact of specific expert models is limited relative to the choice of uncertainty estimates, especially the probability of reordering. This was found to significantly bolster the efficiency of systems, reducing the required number of comparisons by approximately 50%.

Additionally, ranking-level uncertainty metrics are validated for their utility in discerning low-quality predictions, evidencing the value of incorporating nuanced probabilistic models. The paper demonstrates these outputs across various benchmarks, underscoring their potential application in large-scale natural language processing tasks.

Implications and Future Directions

The implications of this research extend both practically and theoretically. Practically, the improvement in computational efficiency via enhanced uncertainty estimations holds promise for applications where inference costs are prohibitive. Theoretically, the generalised framework proposed offers a substrate upon which future advancements can build, particularly in terms of leveraging different probabilistic distributions and expert combinations.

The potential for future exploration includes the development of more sophisticated mechanisms for uncertainty estimation and the adaptation of these methods for use in other domains beyond LLM scoring. Additionally, extending this framework to evaluate LLMs in scenarios with incomplete or noisy comparative data represents a viable next step.

By refining the probabilistic structuring and uncertainty paradigms, this paper contributes a substantial advancement to the field's understanding of how LLMs can be judiciously assessed and utilized across myriad tasks.