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The paper introduces a novel approach using Poly-Encoders for computationally efficient automated creativity assessment, achieving performance comparable to Large Language Models with significantly reduced computational demands.
IntElicit is a framework that uses dialogue policy optimization with a decomposed process reward mechanism to elicit and assess contextualized creativity through adaptive AI interviewing, reducing confounders like domain knowledge and engagement. Experiments show it improves creative outcomes over static assessment methods.