Steering LLMs for Culturally Localized Generation
LLMs are deployed globally, yet produce responses biased towards cultures with abundant training data. Existing cultural localization approaches such as prompting or post-training alignment are black-box, hard to control, and do not reveal whether failures reflect missing knowledge or poor elicitation. In this paper, we address these gaps using mechanistic interpretability to uncover and manipulate cultural representations in LLMs. Leveraging sparse autoencoders, we identify interpretable features that encode culturally salient information and aggregate them into Cultural Embeddings (CuE). We use CuE both to analyze implicit cultural biases under underspecified prompts and to construct wh...
| Authors | Simran Khanuja, Hongbin Liu, Shujian Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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