LLM-as-Judge on a Budget
arXiv:2602.15481v1 Announce Type: new Abstract: LLM-as-a-judge has emerged as a cornerstone technique for evaluating large language models by leveraging LLM reasoning to score prompt-response pairs. Since LLM judgments are stochastic, practitioners commonly query each pair multiple times to estimate mean scores accurately. This raises a critical challenge: given a fixed computational budget $B$, how to optimally allocate queries across $K$ prompt-response pairs to minimize estimation error? % We present a principled variance-adaptive approach leveraging multi-armed bandit theory and concentration inequalities. Our method dynamically allocates queries based on estimated score variances, concentrating resources where uncertainty is highest. Further, our algorithm is shown to achieve a worst-case score-estimation error of $\tilde{O}\left(\sqrt{\frac{\sum_{i=1}^K \sigma_i^2}{B}}\right)$, $\sigma_i^2$ being the unknown score variance for pair $i \in [K]$ with near-optimal budget allocation
arXiv:2602.15481v1 Announce Type: new Abstract: LLM-as-a-judge has emerged as a cornerstone technique for evaluating large language models by leveraging LLM reasoning to score prompt-response pairs. Since LLM judgments are stochastic, practitioners commonly query each pair multiple times to estimate mean scores accurately. This raises a critical challenge: given a fixed computational budget $B$, how to optimally allocate queries across $K$ prompt-response pairs to minimize estimation error? % We present a principled variance-adaptive approach leveraging multi-armed bandit theory and concentration inequalities. Our method dynamically allocates queries based on estimated score variances, concentrating resources where uncertainty is highest. Further, our algorithm is shown to achieve a worst-case score-estimation error of $\tilde{O}\left(\sqrt{\frac{\sum_{i=1}^K \sigma_i^2}{B}}\right)$, $\sigma_i^2$ being the unknown score variance for pair $i \in [K]$ with near-optimal budget allocation. % Experiments on \emph{Summarize-From-Feedback} and \emph{HelpSteer2} demonstrate that our method significantly outperforms uniform allocation, reducing worst-case estimation error while maintaining identical budgets. Our work establishes a theoretical foundation for efficient LLM evaluation with practical implications for AI safety, model alignment, and automated assessment at scale.
Executive Summary
The article 'LLM-as-Judge on a Budget' presents a principled variance-adaptive approach to optimally allocate queries across prompt-response pairs to minimize estimation error when evaluating large language models. The proposed method leverages multi-armed bandit theory and concentration inequalities to dynamically allocate queries based on estimated score variances. The authors demonstrate that their approach outperforms uniform allocation, reducing worst-case estimation error while maintaining identical budgets. This work establishes a theoretical foundation for efficient LLM evaluation with practical implications for AI safety, model alignment, and automated assessment at scale. The authors' innovative solution addresses a critical challenge in the field of LLM evaluation, making it a valuable contribution to the literature.
Key Points
- ▸ The article presents a variance-adaptive approach to LLM evaluation
- ▸ The proposed method leverages multi-armed bandit theory and concentration inequalities
- ▸ The approach outperforms uniform allocation in reducing worst-case estimation error
Merits
Strength in Theoretical Foundation
The article provides a rigorous theoretical framework for efficient LLM evaluation, which is essential for establishing a solid foundation in this field.
Innovative Algorithm
The proposed algorithm is a novel application of multi-armed bandit theory and concentration inequalities to LLM evaluation, making it a significant contribution to the literature.
Demerits
Limited Experimental Scope
The article only presents experiments on two datasets, which may limit the generalizability of the findings and the scope of the proposed approach.
Computational Complexity
The proposed algorithm may have higher computational complexity compared to uniform allocation, which could be a limitation in certain scenarios.
Expert Commentary
The article presents a significant contribution to the field of LLM evaluation by providing a principled variance-adaptive approach to optimally allocate queries across prompt-response pairs. The proposed method leverages multi-armed bandit theory and concentration inequalities, which is an innovative application of these techniques to LLM evaluation. While the article has some limitations, such as limited experimental scope and higher computational complexity, the proposed approach has significant implications for AI safety and model alignment, as well as automated assessment at scale. The article's findings and proposed approach may inform policy decisions related to AI development and deployment, particularly in the context of LLM evaluation and assessment.
Recommendations
- ✓ Future research should investigate the application of the proposed approach to a broader range of datasets and scenarios.
- ✓ The authors should provide a more detailed analysis of the computational complexity of the proposed algorithm and its implications for large-scale LLM evaluation.
