arXiv:2605.23362v1 Announce Type: new Abstract: Evaluating large language models increasingly relies on LLM-as-a-judge protocols, but such evaluations remain costly: different judges have different prices and reliabilities, and the difficulty of each prompt-response pair can vary substantially. This raises a basic allocation question: under a fixed budget, how should one distribute evaluation queries across heterogeneous judges and instances to obtain the most accurate score estimates? We formalize this question as *budgeted heteroskedastic multi-judge estimation*. Given $K$ prompt-response pa
The proliferation of LLM-as-a-judge protocols is making LLM evaluation increasingly complex and costly, necessitating optimized resource allocation strategies.
Effective and cost-efficient evaluation methods are critical for the continued development and deployment of reliable large language models across all industries.
The focus is shifting towards more sophisticated, budget-constrained evaluation methodologies that account for the heterogeneity of LLM judges and prompt difficulties.
- · AI developers
- · LLM evaluation platforms
- · Organizations deploying LLMs
- · Inefficient LLM evaluation methods
- · Undifferentiated LLM judges
More accurate and cost-effective LLM evaluations become widespread.
This leads to faster iteration and improvement cycles for large language models.
The overall quality and trustworthiness of AI systems accelerate, expanding their applications and adoption.
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Read at arXiv cs.LG