Losses that Cook: Topological Optimal Transport for Structured Recipe Generation
Summary: arXiv:2601.02531v2 Announce Type: replace-cross
Abstract
Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural coherence, as well as the correct composition of ingredients. Standard training procedures are primarily based on cross-entropy and focus solely on fluency. Building on RECIPE-NLG, we investigate the use of several composite objectives and present a new topological loss that represents ingredient lists as point clouds in embedding space, minimizing the divergence between predicted and gold ingredients. Using both standard NLG metrics and recipe-specific metrics, we find that our loss significantly improves ingredient- and action-level metrics. Meanwhile, the Dice loss excels in time/temperature precision, and the mixed loss yields competitive trade-offs with synergistic gains in quantity and time. A human preference analysis supports our finding, showing our model is preferred in 62% of the cases.
Introduction
In the realm of natural language generation (NLG), the creation of cooking recipes poses unique challenges that extend beyond mere fluency. The intricacies of culinary artistry demand a deep understanding of not only the language but also the science of cooking itself. This article delves into a novel approach to recipe generation that leverages topological optimal transport, aiming to enhance the quality of generated recipes significantly.
Methodology
Our research builds upon the existing framework of RECIPE-NLG, introducing a new topological loss function designed to optimize the ingredient representation in the embedding space. This method conceptualizes ingredient lists as point clouds, enabling a more nuanced understanding of their relationships and minimizing divergence between predicted and actual ingredients.
Key Findings
- Improvement in ingredient and action-level metrics through the application of topological loss.
- Enhanced precision in time and temperature management using Dice loss.
- Mixed loss function providing advantageous trade-offs between quantity and time efficiency.
- Human preference analysis indicating a preference for our model in 62% of evaluated cases.
Conclusion
The integration of topological loss into the recipe generation process marks a significant advancement in the field of NLG. By addressing the multifaceted requirements of culinary recipes, our approach not only enhances fluency but also ensures procedural accuracy and ingredient fidelity. As artificial intelligence continues to evolve, the implications of our findings could transform how recipes are generated, offering a richer, more engaging experience for culinary enthusiasts.
Future Work
As we move forward, further exploration into the application of topological loss in other domains of NLG could yield valuable insights. Additionally, refining our model based on user feedback and expanding its capabilities to include diverse cooking styles and dietary restrictions will be pivotal in enhancing its utility.