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🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination

Paper · 2026 · Moran Mizrahi, Chen Shani, Gabriel Stanovsky et al.

Summary

LLMs write fluent text but tend to repeat familiar patterns when asked for original ideas. Raising temperature only adds token-level randomness and does not produce new concepts. The authors move the creative step outside the model. An LLM turns textual ideas into structured representations, an algorithm recombines those structures, and the LLM turns the best hybrids back into prose. They apply this to cooking with DishCOVER. Recipes for two seed dishes from Recipe1M+ are parsed into trees whose leaves are ingredients. An edit-distance procedure then gradually transforms one tree into the other, and its intermediate states become blended candidates. These candidates are checked for value, ranked by novelty and rewritten as complete recipes. Automatic metrics and culinary experts compared the outputs with GPT-4o. DishCOVER's recipes were far more novel and diverse while staying mostly coherent and feasible, so they were judged more creative overall. The paradigm could carry over to other fields where ideas have a clear structure and value can be assessed.

Key points

  • Seed ideas: 100 popular dishes were taken from Recipe1M+, averaging about 2,576 recipes each. For each dish, 30 recipes were sampled (3K in total): 15 at random and 15 chosen for diversity. Diversity selection used a Gaussian Mixture Model over embeddings from a Sentence-BERT model fine-tuned on recipes.
  • Pipeline: seed sampling → text-to-tree parsing → edit-distance recombination → value filtering and novelty ranking → tree-to-text generation by an LLM.
  • Core contribution: a new recombination operator that partially converts one structured idea into another, which gives explicit and controllable blending. Genetic crossover and latent-space interpolation lack this control.
  • Results vs. GPT-4o: DishCOVER was significantly better on novelty and diversity. Both systems produced mostly valuable recipes, but GPT-4o scored higher on value in an open-ended task.
  • Released resources: a 5K-recipe dataset generated by DishCOVER plus the code (GitHub: moranmiz/Cooking-Up-Creativity). The paper appeared in TACL vol. 14 (2026).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Stanford UniversityStanford · United States434 works · 314,419 citations🎓Hebrew University of JerusalemJerusalem · Israel65 works · 9,387 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Moran Mizrahi, Chen Shani, Gabriel Stanovsky, Dan Jurafsky and Dafna Shahaf. 2026. 🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.638

@article{mizrahi2026cooking,
  title = {{🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination}},
  author = {Moran Mizrahi, Chen Shani, Gabriel Stanovsky, Dan Jurafsky and Dafna Shahaf},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.638},
  url = {https://doi.org/10.1162/tacl.a.638}
}

References

92 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler. 2024. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI Conferen… find
  2. Gautam Ahuja and Curba Morris Lampert. 2001. Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal, 22(6–7):521–543. https://doi.org/10.1002/smj.176 — [doi:10.1002/smj.176](https://doi.org/10.1002/smj.176) ↗
  3. Michał Bień, Michał Gilski, Martyna Maciejewska, Wojciech Taisner, Dawid Wisniewski, and Agnieszka Lawrynowicz. 2020. RecipeNLG: A cooking recipes dataset for semi-structured text generation. Proceedings of the 13th International Conference on Natural Language Generation, pages 22–28. https://doi.o… ↗
  4. Philip Bille. 2005. A survey on tree edit distance and related problems. Theoretical Computer Science, 337(1-3):217–239. https://doi.org/10.1016/j.tcs.2004.12.030 ↗
  5. Margaret A. Boden. 2004. The Creative Mind: Myths and Mechanisms. Routledge. find
  6. John Chung, Ece Kamar, and Saleema Amershi. 2023. Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 575–59…
  7. David W. Corne and Peter J. Bentley. 2001. Creative Evolutionary Systems. Elsevier. find
  8. Margaret A. Boden. 2009. Computer models of creativity. AI Magazine, 30(3):23–23. https://doi.org/10.1609/aimag.v30i3 .2254 — [doi:10.1609/aimag.v30i3](https://doi.org/10.1609/aimag.v30i3) ↗
  9. Erol Cromwell, Jonah Galeota-Sprung, and Raghuram Ramanujan. 2015. Computational creativity in the culinary arts. In FLAIRS, pages 38–42. find
  10. Tuhin Chakrabarty, Philippe Laban, Divyansh Agarwal, Smaranda Muresan, and Chien-Sheng Wu. 2024a. Art or artifice? Large language models and the false promise of creativity. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, pages 1–34. https://doi.org/10.1145 /3613904.… find
  11. Michael Scott Cuthbert and Christopher Ariza. 2010. music21: A toolkit for computer-aided musicology and symbolic music data. In 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pages 637–642. find
  12. Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song. 2018. Syntax-directed variational autoencoder for structured data. In International Conference on Learning Representations. find
  13. Tuhin Chakrabarty, Vishakh Padmakumar, Faeze Brahman, and Smaranda Muresan. 2024b. Creativity support in the age of large language models: An empirical study involving professional writers. In Proceedings of the 16th Conference on Creativity & Cognition, pages 132–155. https://doi.org /10.1145/36356… — [doi:10.1145/3635636.3656201](https://doi.org/10.1145/3635636.3656201) ↗
  14. John L. Dennis and Aldo Stella. 2011. Teaching creativity: The case for/against genetic algorithms as a model of human creativity. The Open Educational Journal, 4(1):36–40. https://doi.org/10.2174/1874920801104010036 ↗
  15. Hung-Fu Chang and Tong Li. 2025. A framework for collaborating a large language model tool in brainstorming for triggering creative thoughts. Thinking Skills and Creativity, page 101755. https://doi.org/10 .1016/j.tsc.2025.101755 find
  16. Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu, Tian Feng, Yujiu Yang, and Rongsheng Zhang. 2024. HoLLMwood: Unleashing the creativity of large language models in screenwriting via role playing. In Findings of the Association for Computational Linguist… find
  17. Sung-Bae Cho. 2002. Towards creativeevolutionary systems with interactive genetic algorithm. Applied Intelligence, 16:129–138. https://doi.org/10.1023/A:1013614519179 — [doi:10.1023/a:1013614519179](https://doi.org/10.1023/a:1013614519179) ↗
  18. Simona Doboli, Jared Kenworthy, Paul Paulus, Ali Minai, and Alex Doboli. 2020. A cognitive inspired method for assessing novelty of short-text ideas. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE. https://doi.org/10 .1109/IJCNN48605.2020.9206788 find
  19. David K. Elson. 2012. Modeling Narrative Discourse. Columbia University. find
  20. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889–898. https://doi.org/10.18653/v1/P18-1082 ↗
  21. Gilles Fauconnier and Mark Turner. 2003. Conceptual blending, form and meaning. find
  22. Recherches en Communication, 19:57–86. https://doi.org/10.14428/rec.v19i19 .48413 — [doi:10.14428/rec.v19i19](https://doi.org/10.14428/rec.v19i19) ↗
  23. Chao Feng, Xinyu Zhang, and Zichu Fei. 2023. Knowledge solver: Teaching LLMs to search for domain knowledge from knowledge graphs. arXiv preprint arXiv:2309.03118v1. the Arts, 12(2):144. https://doi.org/10 .1037/aca0000125 — [arXiv:2309.03118](https://arxiv.org/abs/2309.03118) find
  24. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. In International Conference on Learning Representations.
  25. Alyssa Hwang, Bryan Li, Zhaoyi Hou, and Dan Roth. 2023a. Large language models as sous chefs: Revising recipes with GPT-3. arXiv preprint arXiv:2306.13986v1. find
  26. Ronald A. Finke, Thomas B. Ward, and Steven M. Smith. 1996. Creative Cognition: Theory, Research, and Applications. MIT Press. find
  27. Giorgio Franceschelli and Mirco Musolesi. 2024. On the creativity of large language models. AI & Society, pages 1–11. find
  28. EunJeong Hwang, Veronika Thost, Vered Shwartz, and Tengfei Ma. 2023b. Knowledge graph compression enhances diverse commonsense generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 558–572. https://doi.org/10.18653/v1/2023 .emnlp-main.37 ↗
  29. Naomi K. Fukagawa, Kyle McKillop, Pamela R. Pehrsson, Alanna Moshfegh, James Harnly, and John Finley. 2022. USDA’s FoodData Central: What is it and why is it needed today? The American Journal of Clinical Nutrition, 115(3):619–624. https://doi.org/10 .1093/ajcn/nqab397, PubMed: 34893796 find
  30. Ovidiu Ivanciuc and Alexandru T. Balaban. 2000. The graph description of chemical structures. In Topological Indices and Related Descriptors in QSAR and QSPR, pages 69–178. CRC Press. https://doi.org/10.1201/9781482296945-9 ↗
  31. Neelansh Garg, Apuroop Sethupathy, Rudraksh Tuwani, Rakhi Nk, Shubham Dokania, Arvind Iyer, Ayushi Gupta, Shubhra Agrawal, Navjot Singh, Shubham Shukla, Kriti Kathuria, Rahul Badhwar, Rakesh Kanji, Anupam Jain, Avneet Kaur, Rashmi Nagpal, and Ganesh Bagler. 2018. FlavorDB: A database of flavor molec… — [doi:10.1093/nar/gkx957](https://doi.org/10.1093/nar/gkx957) find
  32. Jermsak Jermsurawong and Nizar Habash. 2015. Predicting the structure of cooking recipes. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 781–786. https://doi.org/10.18653/v1/D15-1090 ↗
  33. Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen. 2023. StructGPT: A general framework for large language model to reason over structured data. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9237–9251. https://doi.org/10… find
  34. Michael Good. 2001. MusicXML for notation and analysis. The Virtual Score: Representation, Retrieval, Restoration, 12(113–124):160. find
  35. Joy Paul Guilford. 1967. The Nature of Human Intelligence. McGraw-Hill. find
  36. Helena H. Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, and Lav R. Varshney. 2020. RecipeGPT: Generative pre-training based cooking recipe generation and evaluation system. In Companion Proceedings of the Web Conference 2020, pages 181–184. https://doi.org /10.114… — [doi:10.1145/3366424.3383536](https://doi.org/10.1145/3366424.3383536) ↗
  37. Xuhui Jiang, Yuxing Tian, Fengrui Hua, Chengjin Xu, Yuanzhuo Wang, and Jian Guo. 2024. A survey on large language model hallucination via a creativity perspective. arXiv preprint arXiv:2402.06647v1. — [arXiv:2402.06647](https://arxiv.org/abs/2402.06647) find
  38. Anna Katerina Jordanous. 2012. Evaluating Computational Creativity: A Standardised Procedure for Evaluating Creative Systems and Its Application. University of Kent (United Kingdom). find
  39. David J. P. Heinen and Dan R. Johnson. 2018. Semantic distance: An automated measure of creativity that is novel and appropriate. Psychology of Aesthetics, Creativity, and Yoed N. Kenett. 2019. What can quantitative measures of semantic distance tell us about creativity? Current Opinion in Behaviora… — [doi:10.18653/v1/2025](https://doi.org/10.18653/v1/2025) find
  40. Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon, Jaemin Jo, Juho Kim, and Jinwook Seo. 2023. Large-scale text-to-image generation models for visual artists’ creative works. In Proceedings of the 28th International Conference on Intelligent User Interfaces, pages 919–933. https://doi.org/10 .1145/3581641.3584… find
  41. Peihua Ma, Shawn Tsai, Yiyang He, Xiaoxue Jia, Dongyang Zhen, Ning Yu, Qin Wang, Jaspreet K. C. Ahuja, and Cheng-I Wei. 2024. Large language models in food science: Innovations, applications, and future. Trends in Food Science & Technology, page 104488. https://doi.org/10 .1016/j.tifs.2024.104488 find
  42. Arthur Koestler. 1964. The Act of Creation. London Hutchinson. find
  43. Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato. 2017. Grammar variational autoencoder. In International Conference on Machine Learning, pages 1945–1954. PMLR. find
  44. Hirokuni Maeta, Tetsuro Sasada, and Shinsuke Mori. 2015. A framework for procedural text understanding. In Proceedings of the 14th International Conference on Parsing Technologies, pages 50–60. https://doi.org/10 .18653/v1/W15-2206 find
  45. Carolyn Lamb, Daniel G. Brown, and Charles L. A. Clarke. 2018. Evaluating computational creativity: An interdisciplinary tutorial. ACM Computing Surveys (CSUR), 51(2):1–34. https://doi.org/10.1145/3167476 — [doi:10.1145/3167476](https://doi.org/10.1145/3167476) ↗
  46. Yinghao Li, Rampi Ramprasad, and Chao Zhang. 2024. A simple but effective approach to improve structured language model output for information extraction. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5133–5148. https://doi.org/10.18653/v1/2024.findings -emnlp.295 ↗
  47. Chenzhengyi Liu, Jie Huang, Kerui Zhu, and Kevin Chen-Chuan Chang. 2023. DimonGen: Diversified generative commonsense reasoning for explaining concept relationships. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4719–4731. h… find
  48. Alessandro Margherita, Gianluca Elia, and Mark Klein. 2021. Managing the COVID-19 emergency: A coordination framework to enhance response practices and actions. Technological Forecasting and Social Change, 166:120656. https://doi.org/10.1016/j.techfore.2021.120656, PubMed: 33551496 ↗
  49. Javier Marın, Aritro Biswas, Ferda Ofli, Nicholas Hynes, Amaia Salvador, Yusuf Aytar, Ingmar Weber, and Antonio Torralba. 2021. Recipe1M+: A dataset for learning cross-modal embeddings for cooking recipes and food images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1):187–203.… — [doi:10.1109/tpami.2019](https://doi.org/10.1109/tpami.2019) find
  50. Pronita Mehrotra, Aishni Parab, and Sumit Gulwani. 2024. Enhancing creativity in large language models through associative thinking strategies. arXiv preprint arXiv:2405.06715v1. — [arXiv:2405.06715](https://arxiv.org/abs/2405.06715) find
  51. Ye Liu, Yao Wan, Lifang He, Hao Peng, and Philip S. Yu. 2021. KG-BART: Knowledge graph-augmented BART for generative commonsense reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 6418–6425. https://doi.org/10.1609/aaai.v35i7.16796 — [doi:10.1609/aaai.v35i7.16796](https://doi.org/10.1609/aaai.v35i7.16796) ↗
  52. Piotr Mirowski, Kory W. Mathewson, Jaylen Pittman, and Richard Evans. 2023. Co-writing screenplays and theatre scripts with language models: Evaluation by industry professionals. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Yining Lu, Dixuan Wang, Tianjian Li, Don… find
  53. Juan Ramos. 2003. Using TF-IDF to determine word relevance in document queries. In Proceedings of the First Instructional Conference on Machine Learning, volume 242, pages 29–48. Citeseer. find
  54. Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, and Gabriel Stanovsky. 2024. State of what art? A call for multi-prompt llm evaluation. Transactions of the Association for Computational Linguistics, 12:933–949. https://doi.org/10 .1162/tacl_a_00681 find
  55. Sharif Uddin Ahmed Rana and Adrian David Cheok. 2025. Generative innovation: Leveraging the power of large language models for brainstorming. In The Economics of Talent Management and Human Capital, pages 175–192. IGI Global. https://doi.org/10.4018/978-1-6684-6641-4 .ch011 — [doi:10.4018/978-1-6684-6641-4](https://doi.org/10.4018/978-1-6684-6641-4) ↗
  56. Moran Mizrahi and Dafna Shahaf. 2021. 50 ways to bake a cookie: Mapping the landscape of procedural texts. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 1304–1314. https://doi.org/10 .1145/3459637.3482405 find
  57. Sekharipuram S. Ravi, Daniel J. Rosenkrantz, and Giri Kumar Tayi. 1994. Heuristic and special case algorithms for dispersion problems. Operations Research, 42(2):299–310. https://doi.org/10.1287/opre.42.2.299 — [doi:10.1287/opre.42.2.299](https://doi.org/10.1287/opre.42.2.299) ↗
  58. Richard G. Morris, Scott H. Burton, Paul M. Bodily, and Dan Ventura. 2012. Soup over bean of pure joy: Culinary ruminations of an artificial chef. In ICCC, pages 119–125. Citeseer. find
  59. Juan Ramón Rico-Juan and Luisa Micó. 2003. Comparison of AESA and LAESA search algorithms using string and tree-edit-distances. Pattern Recognition Letters, 24(9–10): 1417–1426. https://doi.org/10.1016 /S0167-8655(02)00382-3 find
  60. Michael D. Mumford. 2003. Where have we been, where are we going? Taking stock in creativity research. Creativity Research Journal, 15(2–3):107–120. https://doi.org/10.1080/10400419.2003.9651403 — [doi:10.1080/10400419.2003.9651403](https://doi.org/10.1080/10400419.2003.9651403) ↗
  61. Maite Palomo-Vadillo, Ana-Lucia Ortega-Larrea, Marı́a-Julia Bordonado-Bermejo, and Carmen De-Pablos-Heredero. 2025. Developing an index for measuring gender lens investing in organizations: The GLIMETRICS framework. Frontiers in Psychology, 16:1534355. https://doi.org/10.3389/fpsyg.2025 .1534355, Pu… — [doi:10.3389/fpsyg.2025](https://doi.org/10.3389/fpsyg.2025) ↗
  62. Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024. Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering, 36(7):3580–3599. https://doi.org/10 .1109/TKDE.2024.3352100
  63. Graeme Ritchie. 2007. Some empirical criteria for attributing creativity to a computer program. Minds and Machines, 17:67–99. https://doi.org/10.1007/s11023-007-9066-2 — [doi:10.1007/s11023-007-9066-2](https://doi.org/10.1007/s11023-007-9066-2) ↗
  64. Sameh Said-Metwaly, Wim Van den Noortgate, and Eva Kyndt. 2017. Approaches to measuring creativity: A systematic literature review. Creativity: Theories - Research - Applications, 4(2):238–275. https://doi.org/10.1515/ctra-2017-0013 — [doi:10.1515/ctra-2017-0013](https://doi.org/10.1515/ctra-2017-0013) ↗
  65. Anurag Sarkar and Seth Cooper. 2021. Generating and blending game levels via quality-diversity in the latent space of a variational autoencoder. In Proceedings of the 16th International Conference on the Foundations of Digital Games, pages 1–11. https://doi.org/10.1145/3472538.3472545 ↗
  66. Max Peeperkorn, Tom Kouwenhoven, Dan Brown, and Anna Jordanous. 2024. Is temperature the creativity parameter of large language models? In ICCC. find
  67. Keith R. Sawyer and Danah Henriksen. 2024. Explaining Creativity: The Science of Human Innovation. Oxford University Press. https://doi.org/10.1093/oso/9780197747537.001.0001 — [doi:10.1093/oso](https://doi.org/10.1093/oso) ↗
  68. Francisco C. Pereira and Amı́lcar Cardoso. 2006. Experiments with free concept generation in Divago. Knowledge-Based Systems, 19(7):459–470. https://doi.org/10.1016/j.knosys.2006.04.008 — [doi:10.1016/j.knosys.2006.04.008](https://doi.org/10.1016/j.knosys.2006.04.008) ↗
  69. Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. 2023. Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting. In The Twelfth International Conference on Learning Representations. 63(1):7–1. https://doi.org… ↗
  70. Tony Veale and Amı́lcar Cardoso. 2019. Computational Creativity: The Philosophy and Engineering of Autonomously Creative Systems. Springer. https://doi.org/10 .1007/978-3-319-43610-4 find
  71. Douglas Summers-Stay, Clare R. Voss, and Stephanie M. Lukin. 2023. Brainstorm, then select: A generative language model improves its creativity score. In The AAAI-23 Workshop on Creative AI Across Modalities. find
  72. Anton Voronov, Lena Wolf, and Max Ryabinin. 2024. Mind your format: Towards consistent evaluation of in-context learning improvements. In Findings of the Association for Computational Linguistics ACL 2024, pages 6287–6310. https://doi.org/10 .18653/v1/2024.findings-acl.375 find
  73. Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel Ni, Heung-Yeung Shum, and Jian Guo. 2023. Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. In The Twelfth International Conference on Learning Representations. find
  74. Qian Wan, Siying Hu, Yu Zhang, Piaohong Wang, Bo Wen, and Zhicong Lu. 2024. ‘‘It felt like having a second mind’’: Investigating human-AI co-creativity in prewriting with large language models. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1):1–26. https://doi.org/10.1145/3637361 — [doi:10.1145/3637361](https://doi.org/10.1145/3637361) ↗
  75. Anna Thomas, Adam Yee, Andrew Mayne, Maya B Mathur, Dan Jurafsky, and Kristina Gligorić. 2025. What can large language models do for sustainable food? In Forty-second International Conference on Machine Learning. find
  76. Yuanhe Tian, Fei Xia, and Yan Song. 2024a. Large language models are no longer shallow parsers. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7131–7142. https://doi.org/10 .18653/v1/2024.acl-long.384 find
  77. Dawei Wang, Difang Huang, Haipeng Shen, and Brian Uzzi. 2024a. A preliminary, large-scale evaluation of the collaborative potential of human and machine creativity. PsyArXiv September, 28. find
  78. Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr. 2024b. Knowledge graph prompting for multi-document question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19206–19214. https://doi.org/10.1609/aaai.v38i17 .29889 — [doi:10.1609/aaai.v38i17](https://doi.org/10.1609/aaai.v38i17) ↗
  79. Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, and Nanyun Peng. 2024b. Are large language models capable of generating human-level narratives? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17659–17681…
  80. Zhikun Wu, Thomas Weber, and Florian Müller. 2025. One does not simply meme alone: Evaluating co-creativity between LLMs and humans in the generation of humor. In Proceedings of the 30th International Conference on Intelligent User Interfaces, pages 1082–1092. https://doi.org/10 .1145/3708359.37120… find
  81. James M. Utterback. 1996. Mastering the Dynamics of Innovation. Harvard Business School Press. find
  82. Josep Valls-Vargas, Jichen Zhu, and Santiago Ontanón. 2017. Towards automatically extracting story graphs from natural language stories. In AAAI Workshops. find
  83. Zhun Yang, Adam Ishay, and Joohyung Lee. 2023. Coupling large language models with logic programming for robust and general reasoning from text. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5186–5219. https://doi.org/10 .18653/v1/2023.findings-acl.321 find
  84. Lav R. Varshney, Florian Pinel, Kush R. Varshney, Debarun Bhattacharjya, Angela Schörgendorfer, and Y.-M. Chee. 2019. A big data approach to computational creativity: The curious case of chef watson. IBM Journal of Research and Development, Matthew Yee-King. 2022. Latent spaces: A creative approach… — [doi:10.1007/978-3-031-10960-7](https://doi.org/10.1007/978-3-031-10960-7) find
  85. Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito. 2022. Wordcraft: Story writing with large language models. In Proceedings of the 27th International Conference on Intelligent User Interfaces, pages 841–852. https://doi.org/10 .1145/3490099.3511105 find
  86. Bowen Zhao, Changkai Ji, Yuejie Zhang, Wen He, Yingwen Wang, Qing Wang, Rui Feng, and Xiaobo Zhang. 2023. Large language models are complex table parsers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14786–14802. https://doi.org /10.18653/v1/2023.e… ↗
  87. Yunpu Zhao, Rui Zhang, Wenyi Li, and Ling Li. 2025. Assessing and understanding creativity in large language models. Machine Intelligence Research, 22(3):417–436. https://doi.org/10.1007/s11633-025-1546-4 — [doi:10.1007/s11633-025-1546-4](https://doi.org/10.1007/s11633-025-1546-4) ↗
  88. Shuzhou Yuan and Michael Färber. 2025. Hallucinations can improve large language models in drug discovery. arXiv preprint arXiv:2501.13824v2. — [arXiv:2501.13824](https://arxiv.org/abs/2501.13824) find
  89. Pengfei Zhou, Weiqing Min, Chaoran Fu, Ying Jin, Mingyu Huang, Xiangyang Li, Shuhuan Mei, and Shuqiang Jiang. 2024. Foodsky: A food-oriented large language model that passes the chef and dietetic examination. arXiv preprint arXiv:2406.10261v1. https://doi.org/10.2139/ssrn.4972042 — [doi:10.2139/ssrn.4972042](https://doi.org/10.2139/ssrn.4972042) ↗
  90. Eric Zelikman, Qian Huang, Gabriel Poesia, Noah Goodman, and Nick Haber. 2023. Parsel: Algorithmic reasoning with language models by composing decompositions. Advances in Neural Information Processing Systems, 36:31466–31523. find
  91. Yufan Zhou, Haoyu Shen, and Huan Wang. 2025. FreeBlend: Advancing concept blending with staged feedback-driven interpolation diffusion. arXiv preprint arXiv:2502.05606v2. — [arXiv:2502.05606](https://arxiv.org/abs/2502.05606) find
  92. Jiahuan Zhang, Tianheng Wang, Hanqing Wu, Ziyi Huang, Yulong Wu, Dongbai Chen, Linfeng Song, Yue Zhang, Guozheng Rao, and Kaicheng Yu. 2025. SR-LLM: Rethinking the structured representation in large language model. arXiv preprint arXiv:2502.14352v1. https://doi.org/10.18653/v1/2025.acl-long.172 ↗

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