2 citations · 2 across the 4 of their papers we have counts for
5 papers
Evaluating Cultural Knowledge Processing in Large Language Models: A Cognitive Benchmarking Framework Integrating Retrieval-Augmented Generation
Hung-Shin Lee, Chen-Chi Chang, Ching-Yuan Chen +1
This study proposes a cognitive benchmarking framework to evaluate how large language models (LLMs) process and apply culturally specific knowledge. The framework integrates Bloom'…
Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study
Chu-Hsuan Lee, Chen-Chi Chang, Hung-Shin Lee +2
With many endangered languages at risk of disappearing, efforts to preserve them now rely more than ever on using technology alongside culturally informed teaching strategies. This…
Enhancing Low-Resource Minority Language Translation with LLMs and Retrieval-Augmented Generation for Cultural Nuances
Chen-Chi Chang, Chong-Fu Li, Chu-Hsuan Lee +1
This study investigates the challenges of translating low-resource languages by integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG). Various model co…
Leveraging Retrieval-Augmented Generation for Culturally Inclusive Hakka Chatbots: Design Insights and User Perceptions
Chen-Chi Chang, Han-Pi Chang, Hung-Shin Lee
In an era where cultural preservation is increasingly intertwined with technological innovation, this study introduces a groundbreaking approach to promoting and safeguarding the r…
Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture
Chen-Chi Chang, Ching-Yuan Chen, Hung-Shin Lee +1
This study introduces a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in understanding and processing cultural knowledge, with a spec…