MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System

Recommender systems (RS) are currently being studied to mitigate limitations during cold-start conditions by leveraging modality information or introducing Agent concepts based on the exceptional reasoning capabilities of Large Language Models (LLMs). Meanwhile, food and beverage recommender systems have traditionally used knowledge graph and ontology concepts due to the domain's unique data attributes and relationship characteristics. On this background, we propose MARC, a multimodal and multi-task cocktail recommender system based on Agentic Retrieval-Augmented Generation (RAG) utilizing graph database under cold-start conditions. The proposed system generates high-quality, contextually appropriate answers through two core processes: a task recognition router and a reflection process. The graph database was constructed by processing cocktail data from Kaggle, and its effectiveness was evaluated using 200 manually crafted questions. The evaluation used both LLM-as-a-judge and human evaluation to demonstrate that answers generated via the graph database outperformed those from a simple vector database in terms of quality. The code is available at https://github.com/diddbwls/cocktail_rec_agentrag

Refusal-Feature-guided Teacher for Safe Finetuning via Data Filtering and Alignment Distillation

Recently, major AI service providers such as Google and OpenAI have introduced Finetuning-as-a-Service, which enables users to customize Large Language Models (LLMs) for specific downstream tasks using their own data. However, this service is vulnerable to degradation of LLM safety-alignment when user data contains harmful prompts. While some prior works address this issue, fundamentally filtering harmful data from user data remains unexplored. Motivated by our observation that a directional representation reflecting refusal behavior (called the refusal feature) obtained from safety-aligned LLMs can inherently distinguish between harmful and harmless prompts, we propose the Refusal-Feature-guided Teacher (ReFT). Our ReFT model is trained to identify harmful prompts based on the similarity between input prompt features and its refusal feature. During finetuning, the ReFT model serves as a teacher that filters harmful prompts from user data and distills alignment knowledge into the base model. Extensive experiments demonstrate that our ReFT-based finetuning strategy effectively minimizes harmful outputs and enhances finetuning accuracy for user-specific tasks, offering a practical solution for secure and reliable deployment of LLMs in Finetuning-as-a-Service.