# Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Task

arXiv: https://arxiv.org/abs/2411.01996 · HTML: https://mlsanity.com/paper/2411.01996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 20 of 20)

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- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes](https://mlsanity.com/paper/1809.00812/) (2018)
- [RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System](https://mlsanity.com/paper/2003.02498/) (2020)
- [OLAPH: Improving Factuality in Biomedical Long-form Question Answering](https://mlsanity.com/paper/2405.12701/) (2024)
- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation](https://mlsanity.com/paper/2309.12455/) (2023)
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- [Improved Instruction Ordering in Recipe-Grounded Conversation](https://mlsanity.com/paper/2305.17280/) (2023)
- [RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer](https://mlsanity.com/paper/2210.10628/) (2022)
- [FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation](https://mlsanity.com/paper/2312.03540/) (2023)
- [KitchenScale: Learning to predict ingredient quantities from recipe contexts](https://mlsanity.com/paper/2304.10739/) (2023)
- [CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions](https://mlsanity.com/paper/2405.00523/) (2024)

## Abstract (arXiv)

The advent of Large Language Models (LLMs) have shown promise in various creative domains, including culinary arts. However, many LLMs still struggle to deliver the desired level of culinary creativity, especially when tasked with adapting recipes to meet specific cultural requirements. This study focuses on cuisine transfer-applying elements of one cuisine to another-to assess LLMs' culinary creativity. We employ a diverse set of LLMs to generate and evaluate culturally adapted recipes, comparing their evaluations against LLM and human judgments. We introduce the ASH (authenticity, sensitivity, harmony) benchmark to evaluate LLMs' recipe generation abilities in the cuisine transfer task, assessing their cultural accuracy and creativity in the culinary domain. Our findings reveal crucial insights into both generative and evaluative capabilities of LLMs in the culinary domain, highlighting strengths and limitations in understanding and applying cultural nuances in recipe creation. The code and dataset used in this project will be openly available in urlhttp://github.com/dmis-lab/CulinaryASH.