arXiv:2605.30568v1 Announce Type: new Abstract: LLM-as-a-Judge is a scalable alternative to human evaluation, yet existing rubric-based methods rely on human-annotated data such as reference answers or expert-crafted rubrics. We propose to automatically generate fine-grained evaluation rubrics without any human annotation. Our training-free method generates rubrics at dataset-specific and instance-specific granularities, achieving performance competitive with existing methods across four benchmarks. We further present a method that iteratively fine-tunes a rubric generator model via meta-judge
The rapid advancement and adoption of LLMs necessitate more scalable and objective evaluation methods, moving beyond expensive and inconsistent human annotation.
This development suggests a pathway to more efficient and equitable LLM development, enabling faster iteration and potentially reducing biases inherent in human-centric evaluation processes.
The reliance on human-annotated data for LLM evaluation can now be significantly reduced, allowing for automated, dynamic rubric generation and refinement.
- · AI developers and research labs
- · LLM deployment platforms
- · Quality assurance sector
- · Generative AI industry
- · Companies specializing solely in human data annotation for AI
- · Manual evaluation service providers
Automated evaluation tools for LLMs become more robust and accessible, leading to faster development cycles.
The cost of developing and evaluating complex LLM applications decreases, fostering broader innovation and deployment.
AI systems become more capable of self-correction and continuous improvement, potentially accelerating the development of highly autonomous agents.
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Read at arXiv cs.CL