Abstract Large Language Models (LLMs) excel at many tasks, yet they struggle to produce truly creative, diverse ideas. In this paper, we introduce a novel approach that enhances LLM creativity. We apply LLMs for translating between natural language and structured representations, and perform the core creative leap via cognitively inspired manipulations on these representations. Our notion of creativity goes beyond superficial token-level variations; rather, we recombine structured representations of existing ideas, enabling our system to effectively explore a more abstract landscape of ideas. We demonstrate our approach in the culinary domain with DishCover, a model that generates creative recipes. Experiments and domain-expert evaluations reveal that our outputs, which are mostly coherent and feasible, significantly surpass GPT-4o in terms of novelty and diversity, thus outperforming it in creative generation. We hope our work inspires further research into structured creativity in AI.

Kaitlyn Zhou, Haishan Gao, Sarah Li Chen, Dan Edelstein, Dan Jurafsky, Chen Shani. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Analogy is one of the core capacities of human cognition; when faced with new situations, we often transfer prior experience from other domains.Most work on computational analogy relies heavily on complex, manually crafted input.In this work, we relax the input requirements, requiring only names of entities to be mapped.We automatically extract commonsense representations and use them to identify a mapping between the entities.Unlike previous works, our framework can handle partial analogies and suggest new entities to be added.Moreover, our method's output is easily interpretable, allowing for users to understand why a specific mapping was chosen.Experiments show that our model correctly maps 81.2% of classical 2x2 analogy problems (guess level=50%).On larger problems, it achieves 77.8% accuracy (mean guess level=13.1%).In another experiment, we show our algorithm outperforms human performance, and the automatic suggestions of new entities resemble those suggested by humans.We hope this work will advance computational analogy by paving the way to more flexible, realistic input requirements, with broader applicability.

Chen Shani, Nadav Borenstein, Dafna Shahaf. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL).According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction.This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.