Monte Carlo Tree Search for Recipe Generation using GPT-2
Karan Taneja Richard Segal Richard Goodwin
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.