mlsanityData to 14 Oct 2026

CREATE: Testing LLMs for Associative Creativity

arXiv 2603.09970 ↗First posted 10 Mar 2026cs.CLcited by 7

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Abstract (arXiv)

A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate models' capacity for creative associative reasoning. CREATE requires models to generate sets of paths connecting concepts in a model's parametric knowledge. Paths should have high specificity (distinctiveness and closeness of the concept connection) and high diversity (dissimilarity from other paths), and models are scored more highly if they produce a larger set of strong, diverse paths. This task shares demands of real creativity tasks like hypothesis generation, including an extremely large search space, but enables collection of a sizable benchmark with objective answer grading. Evaluation of frontier models shows that the strongest models achieve higher creative utility than others, with the high multiplicity of answers and complexity of the search making benchmark saturation difficult to achieve. Furthermore, our results illustrate that thinking models are not always more effective on our task, even with high token budgets. Recent approaches for creative prompting give some but limited additional improvement. CREATE provides a sandbox for developing new methods to improve models' capacity for associative creativity.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 27 of its 51 references that are in the map. First posted on arXiv 10 Mar 2026.

Builds on

27 of 51 references.

References · 27

  1. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  2. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  3. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  4. 2023Does Writing with Language Models Reduce Content Diversity?cited by 192
  5. 2024A Roadmap to Pluralistic Alignmentcited by 184
  6. 2023Art or Artifice? Large Language Models and the False Promise of Creativitycited by 172
  7. 2025Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)cited by 141
  8. 2024Homogenization Effects of Large Language Models on Human Creative Ideationcited by 127
  9. 2024DiscoveryBench: Towards Data-Driven Discovery with Large Language Modelscited by 109
  10. 2025Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversitycited by 105
  11. 2025Kosmos: An AI Scientist for Autonomous Discoverycited by 92
  12. 2025The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideascited by 62
15 more
  1. 2025Base Models Beat Aligned Models at Randomness and Creativitycited by 51
  2. 2024Forcing Diffuse Distributions out of Language Modelscited by 48
  3. 2025We're Different, We're the Same: Creative Homogeneity Across LLMscited by 42
  4. 2025LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writingcited by 42
  5. 2024Benchmarking Language Model Creativity: A Case Study on Code Generationcited by 40
  6. 2025HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generationcited by 37
  7. 2024From Distributional to Overton Pluralism: Investigating Large Language Model Alignmentcited by 33
  8. 2025AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprisecited by 33
  9. 2025Roll the dice & look before you leap: Going beyond the creative limits of next-token predictioncited by 29
  10. 2026Towards Execution-Grounded Automated AI Researchcited by 28
  11. 2024IdeaBench: Benchmarking Large Language Models for Research Idea Generationcited by 23
  12. 2024MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledgecited by 20
  13. 2023Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Modelscited by 11
  14. 2025EvalAgent: Discovering Implicit Evaluation Criteria from the Webcited by 11
  15. 2025Measuring Combinatorial Creativity in Large Language Models and Humanscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 7.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Enhancing Creativity in Large Language Models through Associative Thinking Strategies
  2. 2025CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity
  3. 2026Playing with Words, Improving with Rewards: Training Language Models for Creative Association
  4. 2025Training Emergent Joint Associations: A Reinforcement Learning Approach to Creative Thinking in Language Models
  5. 2025AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
  6. 2026LLM Jaggedness Unlocks Scientific Creativity
  7. 2025Universe of Thoughts: Enabling Creative Reasoning with Large Language Models
  8. 2026AGC-Bench: Measuring Artificial General Creativity