Existing Retrieval-Augmented Generation (RAG) systems face challenges in enterprise settings due to limited retrieval scope and data security risks. When relevant internal documents are unavailable, the system struggles to generate accurate and complete responses. Additionally, using closed-source Large Language Models (LLMs) raises concerns about exposing proprietary information. To address these issues, we propose the Secure Multifaceted-RAG (SecMulti-RAG) framework, which retrieves not only from internal documents but also from two supplementary sources: pre-generated expert knowledge for anticipated queries and on-demand external LLM-generated knowledge. To mitigate security risks, we adopt a local open-source generator and selectively utilize external LLMs only when prompts are deemed safe by a filtering mechanism. This approach enhances completeness, prevents data leakage, and reduces costs. In our evaluation on a report generation task in the automotive industry, SecMulti-RAG significantly outperforms traditional RAG—achieving 79.3–91.9% win rates across correctness, richness, and helpfulness in LLM-based evaluation and 56.3–70.4% in human evaluation. This highlights SecMulti-RAG as a practical and secure solution for enterprise RAG.

Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi, Gyu-Hwung Cho, Hwiyeol Jo, Changbong Kim, Hyunwoo Lee, Inho Kang, Sun Kim, Taiwoo Park. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. 2024.

Nayoung Choi, Youngjune Lee, Gyu-Hwung Cho, Haeyu Jeong, Jungmin Kong, Saehun Kim, Keunchan Park, Sarah Cho, Inchang Jeong, Gyohee Nam, Sunghoon Han, Wonil Yang, Jaeho Choi. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. 2024.

Artificial intelligence (AI) is increasingly framed as a collaborative partner in creative activities, yet children's interactions with AI have largely been studied in AI-led instructional settings rather than co-creative collaboration. This leaves open questions about how children can meaningfully engage with AI through iterative co-creation. We present Tinker Tales, a tangible storytelling system designed with narrative and social-emotional scaffolding to support child-AI collaboration. The system combines a physical storytelling board, NFC-embedded toys representing story elements (e.g., characters, places, items, and emotions), and a mobile app that mediates child-AI interaction. Children shape and refine stories by placing and moving story elements and interacting with the AI through tangible and voice-based interaction. We conducted an exploratory user study with 10 children to examine how they interacted with Tinker Tales. Our findings show that children treated the AI as an attentive, responsive collaborator, while scaffolding supported coherent narrative refinement without diminishing children's agency.