AI RESEARCH
Rethinking Atomic Decomposition for LLM Judges: A Prompt-Controlled Study of Reference-Grounded QA Evaluation
arXiv CS.CL
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ArXi:2603.28005v1 Announce Type: new Atomic decomposition -- breaking a candidate answer into claims before verifying each against a reference -- is a widely adopted design for LLM-based reference-grounded judges. However, atomic prompts are typically richer and longer, making it unclear whether any advantage comes from decomposition or from richer prompting. We study this for benchmark-style completeness-sensitive reference- classification: classifying a candidate as fully ed, partially ed, or uned relative to a supplied reference.