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It spans key tasks such as nutrient estimation, food traffic-light classification, synonym linking, cooking measurement conversion, and food named-entity recognition and linking. FoodBench enables robust performance evaluation across zero-, one-, and few-shot settings, laying the groundwork for trustworthy, domain-adapted language models. This resource supports advances in personalized nutrition, dietary assessment, and food system innovation.   Publicly accessible datasets include Ingredient Synonyms Identification (ingredient_synonyms_identification.txt), Household Cooking Measurements Conversion (household_cooking_measurements_conversion.txt), and Food Named-Entity Recognition and Linking (covering CafeteriaFCD, CafeteriaSA (NER_NEL_CafeteriaSA.txt and NER_NEL_CafeteriaFCD.txt), and Artificial NEL (artifical_NEL_foodon.txt, artificial_NEL_hansard.txt, artifical_NEL_snomedct.txt)). The datasets for Food Traffic-Light System Classification, Recipe Nutrient Estimation, and Ingredient Nutrient Estimation are derived from the Recipe1+ dataset, which can be obtained upon request from the original authors. For these cases, we provide publicly available scripts that generate the corresponding FoodBench datasets, including all seeds required for reproducibility (https://github.com/matejMartinc/FoodyLLM/tree/main/datasets ). We also recommend users to test their own seeds and train and test splits. Interested users may request access to Recipe1+ from its authors; once approved, the derived FoodBench datasets can be shared under the same conditions as the original dataset.  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