Feedback-to-Rubrics: Can We Learn Expert Criteria from Inline Comments?

ArXi:2605.29857v1 Announce Type: new Large language models (LLMs) are increasingly used for writing and review, 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.