[2603.19519] Inducing Sustained Creativity and Diversity in Large Language Models

Computer Science > Computation and Language arXiv:2603.19519 (cs) [Submitted on 19 Mar 2026] Title:Inducing Sustained Creativity and Diversity in Large Language Models Authors:Queenie Luo, Gary King, Michael Puett, Michael D. Smith View a PDF of the paper titled Inducing Sustained Creativity and Diversity in Large Language Models, by Queenie Luo and 3 other authors View PDF HTML (experimental) Abstract:We address a not-widely-recognized subset of exploratory search, where a user sets out on a typically long "search quest" for the perfect wedding dress, overlooked research topic, killer company idea, etc. The first few outputs of current large language models (LLMs) may be helpful but only as a start, since the quest requires learning the search space and evaluating many diverse and creative alternatives along the way. Although LLMs encode an impressive fraction of the world's knowledge, common decoding methods are narrowly optimized for prompts with correct answers and thus return mostly homogeneous and conventional results. Other approaches, including those designed to increase diversity across a small set of answers, start to repeat themselves long before search quest users learn enough to make final choices, or offer a uniform type of "creativity" to every user asking similar questions. We develop a novel, easy-to-implement decoding scheme that induces sustained creativity and diversity in LLMs, producing as many conceptually unique results as desired, even without acce...