MuseScorer: Idea Originality Scoring At Scale

Abstract

An objective, face-valid method for scoring idea originality is to measure each idea’s statistical infrequency within a population—an approach long used in creativity research. Yet, computing these frequencies requires manually bucketing idea rephrasings, a process that is subjective, labor-intensive, error-prone, and brittle at scale. We introduce MuseScorer, a fully automated, psychometrically validated system for frequency-based originality scoring. MuseScorer integrates a Large Language Model (LLM) with externally orchestrated retrieval: given a new idea, it retrieves semantically similar prior idea-buckets and zero-shot prompts the LLM to judge whether the idea fits an existing bucket or forms a new one. These buckets enable frequency-based originality scoring without human annotation. Across five datasets ( N participants =1143 , n ideas =16,294 ), MuseScorer matches human annotators in idea clustering structure (AMI =0.59 ) and participant-level scoring ( r = 0.89 ), while demonstrating strong convergent and external validity. The system enables scalable, intent-sensitive, and human-aligned originality assessment for creativity research.