Conversational interfaces have emerged as an accessible and user-friendly alternative to traditional touch-based self-service kiosks in food-ordering systems. Despite their promise, building such systems remains challenging due to the need for costly data annotation, store-specific model adaptation, and scalable deployment. In this study, we propose a fully automated, end-to-end framework that transforms structured menu databases into high-quality annotated datasets and efficiently deploys store-specific conversational models using a parameter-efficient fine-tuning method. Our approach fine-tunes only 0.9% of the backbone model parameters per store, enabling cost-effective and plug-and-play deployment across diverse environments. To enhance robustness, we further integrate a recommendation module that suggests alternative items when requested menu options are unavailable. Experimental results on data from 27 stores in South Korea demonstrate that our framework consistently outperforms existing data generation baselines in intent classification and slot filling performance, while maintaining high annotation quality. Simulated real-world voice-ordering scenarios confirm the practicality of our framework for rapid, scalable, and accessible deployment in real-world environments.

Recent advancements in large language models have significantly influenced the field of online medical consultations.However, critical challenges remain, such as the generation of hallucinated information and the integration of upto-date medical knowledge.To address these issues, we propose Informatics Llama (ILlama), a novel framework that combines retrievalaugmented generation (RAG) with a structured medical knowledge graph.ILlama incorporates relevant medical knowledge by transforming subgraphs from a structured medical knowledge graph into text for RAG.By generating subgraphs from the medical knowledge graph in advance for RAG, specifically focusing on diseases and symptoms, ILlama enhances the accuracy and relevance of its medical reasoning.This framework enables effective incorporation of causal relationships between symptoms and diseases.Also, it delivers context-aware consultations aligned with user queries.Experimental results on the two medical consultation datasets demonstrate that ILlama outperforms strong baselines, achieving a semantic similarity F1 score of 0.884 when compared to ground-truth consultation answers.Furthermore, qualitative analysis of ILlama's responses reveals significant improvements in hallucination reduction and clinical usefulness.These results suggest that ILlama has strong potential as a reliable tool for real-world medical consultation environments.Our implementation is available at:

Open Directory Project (ODP) has been successfully utilized in text classification due to its representation ability of various categories. However, ODP includes a limited number of entities, which play an important role in classification tasks. In this paper, we enrich the semantics of ODP categories with Probase entities. To effectively incorporate Probase entities in ODP categories, we first represent each ODP category and Probase entity in terms of concepts. Next, we measure the semantic relevance between an ODP category and a Probase entity based on the concept vector. Finally, we use Probase entity to enrich the semantics of the ODP categories. Our experimental results show that the proposed methodology exhibits a significant improvement over state-of-the-art techniques in the ODP-based text classification.