| Challenge: | Existing methods to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. |
| Approach: | They propose to expand a name and incomplete ingredient details into complete natural-text instructions aligned with the user’s historical preferences. |
| Outcome: | The proposed model generates plausible recipes from user-aware representations of recipes from 180K recipes and 700K interactions. |
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Assistive Recipe Editing through Critiquing (2023.eacl-main)
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| 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. |
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 . |
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. |
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. |
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. |
Routing Enforced Generative Model for Recipe Generation (2020.emnlp-main)
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| Challenge: | Existing methods to generate recipes with information about ingredients are difficult to use in practice. |
| Approach: | They propose a routing method to dive into the content selection under the internal restrictions. |
| Outcome: | The proposed model improves on BLEU, F1 and human evaluation. |
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. |
Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
| Approach: | They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on IMDb, Yelp and Longformer benchmarks. |
GRAVITY: A Framework for Personalized Text Generation via Profile-Grounded Synthetic Preferences (2026.eacl-long)
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| Challenge: | Personalization in LLMs often relies on costly human feedback or interaction logs, limiting scalability and neglecting deeper user attributes. |
| Approach: | They propose a framework for generating synthetic, profile-grounded preference data that captures users’ interests, values, beliefs, and personality traits. |
| Outcome: | The proposed framework improves on book descriptions for 400 Amazon users across multiple cultures, with user studies showing that outputs are preferred over 86% of the time. |
LLMs + Persona-Plug = Personalized LLMs (2025.acl-long)
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Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated extraordinary capabilities in natural language understanding, generation, and reasoning. |
| Approach: | They propose a plug-and-play LLM model that embeds a user-specific embedding for each individual by modeling her historical contexts through a lightweight plug-in user embedder module. |
| Outcome: | Experiments on various tasks in the language model personalization (LaMP) benchmark show that the proposed model significantly outperforms existing personalized LLM approaches. |