LLM-based and hybrid approaches outperform traditional TF-IDF and DeBERTa baselines in recipe nutrient estimation under strict EU tolerances, but with substantially higher inference latency.
This work was partially supported by the National Science and Technology Council, Taiwan, under Grant No
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CGU-ILALab at FoodBench-QA 2026: Comparing Traditional and LLM-based Approaches for Recipe Nutrient Estimation
LLM-based and hybrid approaches outperform traditional TF-IDF and DeBERTa baselines in recipe nutrient estimation under strict EU tolerances, but with substantially higher inference latency.