@inproceedings{ibrahim-etal-2026-recifine,
    title = "{R}eci{F}ine: Finely Annotated Recipe Dataset for Controllable Recipe Generation",
    author = "Ibrahim, Nuhu  and
      Ravikumar, Rishi  and
      Stevens, Robert  and
      Batista-Navarro, Riza",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.210/",
    pages = "4058--4074",
    ISBN = "979-8-89176-386-9",
    abstract = "We introduce ReciFine, the largest human-evaluated, finely annotated recipe dataset to date, designed to advance controllable and trustworthy recipe generation. Existing resources, such as RecipeNLG, extract food items only from ingredient lists, overlooking entities expressed in instructions, including tools, chef actions, food and tool states, and durations, which are crucial for realistic and context-aware generation. To address this limitation, we extend RecipeNLG with finely annotated extraction of over 97 million entities across ten entity types from 2.2 million recipes. We are the first to explore recipe generation with explicit control over multiple entity types, enabling models to generate recipes conditioned not only on ingredients but also on tools, chef actions, cooking durations, and other contextual factors. Large language models fine-tuned or few-shot prompted with ReciFine extractions consistently outperform those trained on ingredient-list data alone across both automatic and human evaluations. ReciFine establishes a foundation for factual, coherent, structured, controllable recipe generation, and we release a human-annotated benchmark to support future evaluation and model development."
}