mlsanityData to 14 Oct 2026

Leveraging Retrieval-Augmented Generation for Culturally Inclusive Hakka Chatbots: Design Insights and User Perceptions

arXiv 2410.15572 ↗First posted 21 Oct 2024cs.CL cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

In an era where cultural preservation is increasingly intertwined with technological innovation, this study introduces a groundbreaking approach to promoting and safeguarding the rich heritage of Taiwanese Hakka culture through the development of a Retrieval-Augmented Generation (RAG)-enhanced chatbot. Traditional large language models (LLMs), while powerful, often fall short in delivering accurate and contextually rich responses, particularly in culturally specific domains. By integrating external databases with generative AI models, RAG technology bridges this gap, empowering chatbots to not only provide precise answers but also resonate deeply with the cultural nuances that are crucial for authentic interactions. This study delves into the intricate process of augmenting the chatbot's knowledge base with targeted cultural data, specifically curated to reflect the unique aspects of Hakka traditions, language, and practices. Through dynamic information retrieval, the RAG-enhanced chatbot becomes a versatile tool capable of handling complex inquiries that demand an in-depth understanding of Hakka cultural context. This is particularly significant in an age where digital platforms often dilute cultural identities, making the role of culturally aware AI systems more critical than ever. System usability studies conducted as part of our research reveal a marked improvement in both user satisfaction and engagement, highlighting the chatbot's effectiveness in fostering a deeper connection with Hakka culture. The feedback underscores the potential of RAG technology to not only enhance user experience but also to serve as a vital instrument in the broader mission of ethnic mainstreaming and cultural celebration.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 8 of its 26 references that are in the map. First posted on arXiv 21 Oct 2024.

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8 of 26 references.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 7

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
  3. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 109
  4. 2023ChatGPT as your Personal Data Scientistcited by 22
  5. 2022A Survey for Efficient Open Domain Question Answeringcited by 20
  6. 2022Language and Culture Internalisation for Human-Like Autotelic AIcited by 14
  7. 2022CINO: A Chinese Minority Pre-trained Language Modelcited by 11

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  1. 2026Co-creation of AI technology, empowering curators of cultural heritage information and guarding research commons
  2. 2026Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks
  3. 2024Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise
  4. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
  5. 2024Deploying Large Language Models With Retrieval Augmented Generation
  6. 2024Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture
  7. 2024FACTS About Building Retrieval Augmented Generation-based Chatbots
  8. 2025From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education