Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

arXiv:2606.11762v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks. However, most existing creativity metrics are tightly coupled to specific tasks, embedding domain assumptions into the evaluation process, and limiting scalability and generality. To address this gap, we introduce an automated, domain-agnostic framework for quantify

Source: arXiv cs.CL — read the full report at the original publisher.

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