Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01649 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918183748763648 |
|---|---|
| author | Lee, Hung-Shin Chang, Chen-Chi Chen, Ching-Yuan Hsu, Yun-Hsiang |
| author_facet | Lee, Hung-Shin Chang, Chen-Chi Chen, Ching-Yuan Hsu, Yun-Hsiang |
| contents | This study proposes a cognitive benchmarking framework to evaluate how large language models (LLMs) process and apply culturally specific knowledge. The framework integrates Bloom's Taxonomy with Retrieval-Augmented Generation (RAG) to assess model performance across six hierarchical cognitive domains: Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating. Using a curated Taiwanese Hakka digital cultural archive as the primary testbed, the evaluation measures LLM-generated responses' semantic accuracy and cultural relevance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Cultural Knowledge Processing in Large Language Models: A Cognitive Benchmarking Framework Integrating Retrieval-Augmented Generation Lee, Hung-Shin Chang, Chen-Chi Chen, Ching-Yuan Hsu, Yun-Hsiang Computation and Language This study proposes a cognitive benchmarking framework to evaluate how large language models (LLMs) process and apply culturally specific knowledge. The framework integrates Bloom's Taxonomy with Retrieval-Augmented Generation (RAG) to assess model performance across six hierarchical cognitive domains: Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating. Using a curated Taiwanese Hakka digital cultural archive as the primary testbed, the evaluation measures LLM-generated responses' semantic accuracy and cultural relevance. |
| title | Evaluating Cultural Knowledge Processing in Large Language Models: A Cognitive Benchmarking Framework Integrating Retrieval-Augmented Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.01649 |