arXiv:2601.08654v2 Announce Type: replace-cross Abstract: Rubric-based text evaluation increasingly uses large language models (LLMs) as scalable judges, but aligning frozen black-box models with human scoring standards remains challenging. We formulate this challenge as a criteria-transfer problem: the goal is not merely to prompt an LLM to assign a score, but to transfer human rubric intent into a stable, auditable, and human-aligned scoring protocol. We identify three recurring failure modes in LLM-based rubric scoring: rubric execution drift, unverifiable score attribution, and human-scale
The proliferation of Large Language Models (LLMs) has led to their widespread application in various tasks, including text evaluation, making their reliability and alignment with human standards a current critical challenge.
Reliable and auditable LLM-based text evaluation is crucial for scaling automated assessment processes across education, content creation, and enterprise, directly impacting efficiency and quality control.
This research outlines a methodology to formalize and stabilize LLM scoring, moving beyond simple prompting to evidence-grounded systems, which could significantly improve the robustness of automated evaluation.
- · AI developers
- · Educational technology sector
- · Content moderation platforms
- · Enterprise workflow automation
- · Manual assessors (for certain tasks)
- · LLMs with black-box evaluation methods
- · Companies relying on unstable scoring protocols
Increased trust and adoption of LLM-based evaluation systems across various industries.
Automation of highly subjective tasks at scale, leading to new service offerings and market efficiencies.
Re-evaluation of traditional human assessment roles and training curricula as LLM capabilities become more sophisticated and auditable.
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Read at arXiv cs.LG