# Monte Carlo Tree Search for Recipe Generation using GPT-2

arXiv: https://arxiv.org/abs/2401.05199 · HTML: https://mlsanity.com/paper/2401.05199/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation](https://mlsanity.com/paper/2206.02369/) (2022)
- [RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System](https://mlsanity.com/paper/2003.02498/) (2020)
- [PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding](https://mlsanity.com/paper/2109.13582/) (2021)
- [Structure-Aware Generation Network for Recipe Generation from Images](https://mlsanity.com/paper/2009.00944/) (2020)
- [A Big Data Approach to Computational Creativity](https://mlsanity.com/paper/1311.1213/) (2013)
- [Food recognition and recipe analysis: integrating visual content, context and external knowledge](https://mlsanity.com/paper/1801.07239/) (2018)
- [ENCONTER: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer](https://mlsanity.com/paper/2103.09548/) (2021)

## Built on by (most cited first, 3 of 3)

- [Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion](https://mlsanity.com/paper/2505.08747/) (2025)
- [KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models](https://mlsanity.com/paper/2505.14629/) (2025)
- [On Recipe Memorization and Creativity in Large Language Models: Is Your Model a Creative Cook, a Bad Cook, or Merely a Plagiator?](https://mlsanity.com/paper/2506.23527/) (2025)

## Abstract (arXiv)

Automatic food recipe generation methods provide a creative tool for chefs to explore and to create new, and interesting culinary delights. Given the recent success of large language models (LLMs), they have the potential to create new recipes that can meet individual preferences, dietary constraints, and adapt to what is in your refrigerator. Existing research on using LLMs to generate recipes has shown that LLMs can be finetuned to generate realistic-sounding recipes. However, on close examination, these generated recipes often fail to meet basic requirements like including chicken as an ingredient in chicken dishes. In this paper, we propose RecipeMC, a text generation method using GPT-2 that relies on Monte Carlo Tree Search (MCTS). RecipeMC allows us to define reward functions to put soft constraints on text generation and thus improve the credibility of the generated recipes. Our results show that human evaluators prefer recipes generated with RecipeMC more often than recipes generated with other baseline methods when compared with real recipes.