| Challenge: | Existing methods for generating recipes that satisfy dietary restrictions are inconsistent or incoherent and paired datasets are not available at scale. |
| Approach: | They propose to build a hierarchical denoising auto-encoder that edits recipes given ingredient-level critiques by interacting with the predicted ingredients. |
| Outcome: | The proposed model can more effectively edit recipes compared to strong language models and iteratively rewrites recipes to satisfy user feedback. |
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SHARE: a System for Hierarchical Assistive Recipe Editing (2022.emnlp-main)
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| Challenge: | Existing recipe websites do not provide options for users with dietary restrictions . a growing population follows some form of dietary restriction, with many people following it for a variety of reasons . |
| Approach: | They propose a system for hierarchical assistive recipe editing that performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. |
| Outcome: | The proposed system can adapt a recipe to satisfy a user-specified dietary constraint. |
Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario (2022.emnlp-main)
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| Challenge: | Existing models fail to learn and use culinary knowledge in a compositional way, argues a new study. |
| Approach: | They propose a task that asks models to modify a base recipe according to the change of an ingredient. |
| Outcome: | The proposed model can perform compositional generalization in a realistic setting . existing models have difficulties in modifying ingredients while preserving original style . |
ReciFine: Finely Annotated Recipe Dataset for Controllable Recipe Generation (2026.findings-eacl)
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| Challenge: | Existing resources, such as RecipeNLG, extract food items only from ingredient lists, overlooking entities expressed in instructions, such tools, chef actions, food and tool states, and durations. |
| Approach: | They extend RecipeNLG to extract 97 million entities from 2.2 million recipes. |
| Outcome: | The proposed model outperforms existing models trained on ingredient-list data on both automatic and human evaluations. |
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks (2020.acl-main)
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| Challenge: | a web-based algorithm can be used to align instructions for different tasks . video instructions can be noisy and contain far more information than textual instructions. |
| Approach: | They propose an algorithm that learns pairwise alignments between different recipes . they then use a graph algorithm to derive a joint alignment between multiple video and text recipes based on the same recipe. |
| Outcome: | The proposed algorithm learns pairwise alignments between different recipes for the same dish. |
Cooking Up a Neural-based Model for Recipe Classification (2020.lrec-1)
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Elham Mohammadi, Nada Naji, Louis Marceau, Marc Queudot, Eric Charton, Leila Kosseim, Marie-Jean Meurs
| Challenge: | a dataset of cooking recipes in French is highly imbalanced due to collaborative nature of the dataset . authors propose a neural-based model to address the first task of the DEFT 2013 shared task . |
| Approach: | They propose a neural-based model to address the first task of the DEFT 2013 shared task . they use state-of-the-art embedding approaches and deep architectures to address imbalanced dataset . |
| Outcome: | The proposed model outperforms models that use only pretrained embeddings in micro and macro F1 scores. |
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models (2025.acl-long)
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| Challenge: | Recent advances in large language models and the abundance of food data have led to studies to improve food understanding using LLMs. |
| Approach: | They propose a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generate recipes with associated micro-nutritional information. |
| Outcome: | The proposed system outperforms existing methods in food recommendation, recipe generation and nutritional analysis. |
Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain (2021.eacl-srw)
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| Challenge: | Recent work in food computing focus on generating new recipes from scratch . however, there are a large number of new recipes generated daily with user reviews . |
| Approach: | They propose to use word embedding models to capture the semantic meaning of recipe ingredients and use them to enrich their data. |
| Outcome: | The proposed engine will use food data to modify a recipe to fit user preferences. |
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)
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| Challenge: | Named entities pose a unique challenge to traditional methods of language modeling. |
| Approach: | They propose a Hierarchically Disentangled Model for named entities in cooking recipes using a dataset from several publicly available online sources. |
| Outcome: | The proposed model is based on 158,473 cooking recipes from public sources. |
RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes (D18-1)
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| Challenge: | Existing comprehension tests for QA are limited by the text sources and questionanswer formats. |
| Approach: | They propose a dataset for multimodal comprehension of cooking recipes . preliminary results indicate RecipeQA will serve as a challenging test bed . |
| Outcome: | The proposed dataset will serve as a test bed and ideal benchmark for evaluating machine comprehension systems. |
Aligning Actions Across Recipe Graphs (2021.emnlp-main)
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| Challenge: | a recipe explains step by step how to cook a dish, but recipes differ in which cooking actions they describe explicitly, how they describe them, and in which order. |
| Approach: | They propose a recipe corpus which annotates cooking steps in recipes at sentence level . they train a neural model to predict recipes on ARA and model it for automatic understanding . |
| Outcome: | The proposed model can predict recipes with fine-grained structural information . it shows that recipes can be explained in different ways, or not at all . |