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CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies

About

To enhance language models' cultural awareness, we design a generalizable pipeline to construct cultural knowledge bases from different online communities on a massive scale. With the pipeline, we construct CultureBank, a knowledge base built upon users' self-narratives with 12K cultural descriptors sourced from TikTok and 11K from Reddit. Unlike previous cultural knowledge resources, CultureBank contains diverse views on cultural descriptors to allow flexible interpretation of cultural knowledge, and contextualized cultural scenarios to help grounded evaluation. With CultureBank, we evaluate different LLMs' cultural awareness, and identify areas for improvement. We also fine-tune a language model on CultureBank: experiments show that it achieves better performances on two downstream cultural tasks in a zero-shot setting. Finally, we offer recommendations based on our findings for future culturally aware language technologies. The project page is https://culturebank.github.io . The code and model is at https://github.com/SALT-NLP/CultureBank . The released CultureBank dataset is at https://huggingface.co/datasets/SALT-NLP/CultureBank .

Weiyan Shi, Ryan Li, Yutong Zhang, Caleb Ziems, Chunhua yu, Raya Horesh, Rog\'erio Abreu de Paula, Diyi Yang• 2024

Related benchmarks

TaskDatasetResultRank
Cultural AlignmentCulturalBench Easy
Accuracy90.8
24
Cultural AlignmentCulturalBench Hard
Accuracy60
24
Cultural AlignmentWVS
Accuracy68.11
24
Cultural AlignmentPRISM
Rating4.323
24
Cultural AlignmentGlobalOpinionQA
Accuracy56.76
24
Cultural ReasoningCultureScope (CS) (test)
Accuracy82.56
10
Cultural ReasoningSAGE (test)
Accuracy96.23
10
Cultural ReasoningCulturalBench-Hard (CB-H) (test)
Accuracy40.13
10
Cultural ReasoningBLEnD (test)
Accuracy77.75
10
Content ModerationContent Moderation Korean (test)
Abusive Rate63.5
4
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