The Culture Funnel: You Can't Align What isn't in the Data
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Computer Science > Computation and Language
Title:The Culture Funnel: You Can't Align What isn't in the Data
Abstract:Current cultural alignment approaches focus on inference-time interventions, assuming models already contain sufficient cultural knowledge. We argue modern LLM pipelines suffer from a cultural data funnel. Using a multidimensional tagging framework across pretraining, fine-tuning, alignment, and reasoning datasets, we show explicit cultural signals decline sharply during post-training, while geographically concentrated, task-specialized data dominates. Multilinguality enhances geographic diversity of cultural knowledge but does not ensure balanced representation. Our tags improve downstream cultural benchmark performance, demonstrating that advances require shifting focus in training data pipelines. To facilitate future research, we release our culturally tagged dataset with 5.6M samples at this https URL.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.13808 [cs.CL] |
| (or arXiv:2606.13808v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.13808
arXiv-issued DOI via DataCite (pending registration)
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