Simran Khanuja

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 (SAEs), we identify interpretable features that encode culturally salient information and aggregate them into $\textbf{Cu}$ltural $\textbf{E}$mbeddings ($\textit{CuE}$). We use $\textit{CuE}$ both to analyze implicit cultural biases given underspecified prompts, and to construct white-box interventions to steer responses towards a target culture. Using $\textit{CuE}$, we find that $\textbf{60\%}$ of the responses for $\textit{Gemma-2-9B}$ default to the United States (U.S.) and United Kingdom (U.K.). Across multiple models, we show that $\textit{CuE}$-based steering increases cultural faithfulness (win rates of $\textbf{48\%}$ vs. 24\% in pairwise comparisons) and elicits significantly rarer, long-tail cultural concepts than prompting alone (win rates of $\textbf{53\%}$ vs. 17\%). Notably, $\textit{CuE}$-based steering is complementary to black-box localization methods, offering gains when applied on top of prompt-augmented inputs. This also suggests that models can benefit by better elicitation strategies, and don't necessarily need to be fine-tuned with more data, though this varies across cultures.

AI systems are used worldwide, but they struggle to serve the needs of culturally diverse populations. Prior work on cultural understanding evaluates AI systems on text-only settings or on visual artifact recognition (e.g. foods, clothing). The ability to reason about visually observable behaviors through local social norms, which we call visual norm understanding, remains unexamined. We introduce NormViz-Bench, a high quality, human-validated benchmark of 3,272 contrastive image pairs (6,544 images) spanning 16 countries. Each pair varies only in the culturally relevant behavior (e.g., objects, attributes, spatial relations, and actions) that alters how each image is interpreted. Each image is labeled as conforming to, violating, or irrelevant to local social norms, and pair-level evaluation requires both images to be correctly classified, thereby preventing reliance on superficial visual shortcuts. Even the strongest VLMs, Gemini 3.0 Flash and Qwen2.5 VL 7B, succeed on only 25.3% and 23.0% of pairs, struggling most with identifying violating and culturally benign visual behaviors. Towards bridging this, we introduce NormViz-Train, a training dataset of 64k images paired with explanations. Though absolute performance remains low (<30%), finetuning on NormViz-Train improves pair accuracy relatively by up to 86% and 36% for Qwen3-VL 4B and 8B respectively, showing a path forward to teach models to connect visual perception to cultural significance. Together, NormViz-Bench and NormViz-Train establish visual norm understanding as a challenging and consequential frontier for multimodal AI.