Large language models (LLMs) are increasingly embedded in development pipelines and the daily workflows of AI practitioners. However, their effectiveness depends on access to high-quality datasets that are sufficiently large, diverse, and contextually relevant. Existing datasets often fall short of these requirements, prompting the use of synthetic data (SD) generation. A critical step in this process is the creation of human seed examples, which guide the generation of SD tailored to specific tasks. We propose a participatory methodology for seed example generation, involving multidisciplinary teams in structured workshops to co-create examples aligned with Responsible AI principles. In a pilot study with a Responsible AI team, we facilitated hands-on activities to produce seed examples and evaluated the resulting data across three dimensions: diversity, sensibility, and relevance. Our findings suggest that participatory approaches can enhance the representativeness and contextual fidelity of synthetic datasets. We provide a reproducible framework to support NLP practitioners in generating high-quality seed data for LLM development and deployment

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 for future culturally aware language technologies. We release the CultureBank dataset, code and models at https://github.com/SALT-NLP/CultureBank. Our project page is at culturebank.github.io