---
title: "🧑‍🍳 Cooking Up Creativity — paper by Mizrahi et al. (2026)"
description: "LLMs write fluent text but tend to repeat familiar patterns when asked for original ideas. 92 references."
authors: ["Moran Mizrahi", "Chen Shani", "Gabriel Stanovsky", "Dan Jurafsky", "Dafna Shahaf"]
year: 2026
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2026.tacl-1.20/"
doi: "10.1162/tacl.a.638"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mizrahi-2026-cooking-up-creativity-enhancing-llm/"
---
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# 🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination

**Authors:** [Moran Mizrahi](https://md.research.elamur.ai/free/en/authors/moran-mizrahi/), [Chen Shani](https://md.research.elamur.ai/free/en/authors/chen-shani/), [Gabriel Stanovsky](https://md.research.elamur.ai/free/en/authors/gabriel-stanovsky/), [Dan Jurafsky](https://md.research.elamur.ai/free/en/authors/dan-jurafsky/) and [Dafna Shahaf](https://md.research.elamur.ai/free/en/authors/dafna-shahaf/) · **Year:** 2026 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2026](https://md.research.elamur.ai/free/en/venues/tacl/2026/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.tacl-1.20/) · [PDF](https://aclanthology.org/2026.tacl-1.20.pdf) · [DOI 10.1162/tacl.a.638](https://doi.org/10.1162/tacl.a.638) · [OpenAlex](https://openalex.org/W7155554873)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## 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.*

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- [Hebrew University of Jerusalem](https://md.research.elamur.ai/free/en/universities/hebrew-university-of-jerusalem/) — University, Jerusalem, Israel · 65 works · 9,387 citations

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## 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.

```bibtex
@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}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/48/)

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*Updated 1 November 2026*