arXiv AI August 28, 2026 By Sam Grouchnikov, Phillip Gregory, Jiho Noh

Using Poly-Encoders for Computationally Efficient Automated Creativity Assessment

The paper presents a new method for automated creativity assessment that uses Poly-Encoders to achieve high accuracy while remaining computationally efficient. By fine‑tuning a Poly-Encoder on a public dataset of 18,000 human‑rated responses from the Scientific Creative Thinking Test, the authors obtain Pearson correlations up to r = 0.74, comparable to large language models. The approach relies on small pre‑trained BERT encoders, dramatically reducing computational demands and enabling potential deployment on consumer‑grade hardware.

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