CulturePark: Boosting Cross-cultural Understanding in Large Language Models
Authors: Cheng Li, Damien Teney, Linyi Yang, Qingsong Wen, Xing Xie, Jindong Wang
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated these models across three downstream tasks: content moderation, cultural alignment, and cultural education. |
| Researcher Affiliation | Collaboration | Cheng Li Institute of Software, CAS EMAIL Damien Teney Idiap Research Institute Linyi Yang Westlake University EMAIL Qingsong Wen Squirrel AI EMAIL Xing Xie Microsoft Research EMAIL Jindong Wang William & Mary EMAIL |
| Pseudocode | Yes | Figure 9: Pipeline of data refinement. |
| Open Source Code | Yes | Code is released at https://github. com/Scarelette/Culture Park. |
| Open Datasets | Yes | The seed questions initiating the communication have two sources: World Values Survey (WVS) [Survey, 2022b] and Global Attitudes surveys (GAS) from Pew Research Center [Survey, 2022a]. |
| Dataset Splits | No | The paper mentions 41k samples used for fine-tuning and a test set for evaluation, but does not explicitly provide training/validation/test splits for the fine-tuning data or the source datasets. |
| Hardware Specification | No | The paper mentions using GPT-3.5-Turbo and fine-tuning Llama-2-70b models but does not provide specific hardware details such as GPU/CPU models, memory, or processor types used for these operations. |
| Software Dependencies | No | The paper mentions using "text-embedding-3-small" and "K-means" but does not provide specific version numbers for key software components or libraries required for replication, nor a comprehensive list of dependencies. |
| Experiment Setup | Yes | Hyperparameters are shown in Table 6. Table 6: Details on Fine-tuning GPT-3.5-turbo using Open AI API. Model [various] Epochs [various numbers]. |