厨艺争霸：使用ASH基准评估大语言模型在菜系迁移任务中的表现
Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Task
AI总结：
本研究提出ASH基准，评估大语言模型在菜系迁移任务中的烹饪创意，通过生成和评估文化适应性食谱，揭示其在文化准确性和创意性上的优势与局限。
摘要：
大语言模型（LLMs）的出现已在包括烹饪艺术在内的多个创意领域展现出潜力。然而，许多LLM在提供所需水平的烹饪创意方面仍存在困难，尤其是在根据特定文化要求调整食谱的任务中。本研究聚焦于菜系迁移——将一种菜系的元素应用于另一种菜系——以评估LLM的烹饪创意能力。我们采用多种LLM来生成和评估文化适应性食谱，并将其评估结果与LLM和人类判断进行比较。我们引入了ASH（真实性、敏感性、和谐性）基准来评估LLM在菜系迁移任务中的食谱生成能力，评估其在烹饪领域的文化准确性和创意性。我们的研究结果揭示了LLM在烹饪领域的生成和评估能力的关键见解，突出了其在理解和应用食谱创作中的文化细微差别方面的优势和局限性。本项目使用的代码和数据集将在\url{http://github.com/dmis-lab/CulinaryASH}公开提供。
英文摘要：
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 \url{http://github.com/dmis-lab/CulinaryASH}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
