arXiv:2605.29857v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for writing and review support, but their usefulness depends on context-dependent criteria, such as expert preferences or organization-specific conventions, that are often tacit, undocumented, and difficult to elicit directly. We propose a problem setting for learning reusable natural-language rubrics from accumulated inline comments on artifacts such as human-written or LLM-generated drafts. Our method infers rubrics from these comments and iteratively refines them by observing comment-wise mism
The proliferation of LLMs in writing and review processes, coupled with the need for context-specific guidance, makes the development of automated rubric learning highly relevant.
This research addresses a critical challenge in LLM application by enabling them to better understand and apply tacit, domain-specific expert criteria, improving their utility in complex tasks.
The ability to automatically generate and refine rubrics from inline comments could significantly enhance the quality and consistency of LLM-generated content and assessments.
- · AI developers
- · Organizations using LLMs for internal review
- · Content creators
- · Education technology
- · Manual rubric creators
- · Generative AI lacking contextual understanding
Further integration of LLMs into content creation and assessment workflows becomes more effective.
Improved customizability of LLM behavior for specific organizational standards and expert preferences.
The development of 'expert AI' that can internalize and apply nuanced human judgment across various domains.
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Read at arXiv cs.LG