arXiv Computation and Language
3d ago

LLMs Infer Cultural Context but Fail to Apply It When Responding

The paper introduces CAPRI, a dataset of conversations designed to test whether large language models (LLMs) can adapt their responses to users’ cultural backgrounds. Experiments show that while LLMs can infer a user’s cultural context and recall relevant conventions, they frequently fail to apply this knowledge when generating answers unless explicitly prompted to do so. The study also finds that LLMs’ cultural adaptations improve as more cues are provided, yet their default priors are not culture‑neutral and sometimes reflect the model’s country of origin.

By Yisong Miao, Jian Zhu, Vered Shwartz
arXiv Computation and Language
Oct 12

The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs

The paper investigates how alignment training in large language models (LLMs) can suppress cultural knowledge rather than erase it. By surveying 1.53 million humans across 16 MENA countries and testing 26 models, the authors find that when models refuse to answer, their internal logit distributions still align closely with human responses, a phenomenon they term the "alignment veto." They distinguish between suppression failures—where accurate internal representations are blocked at output—and representational bias failures—where the encoding itself diverges from human values, and show that these require different interventions. The study also reveals significant inequities in the safety tax, with a 37.6 % cost and a 19.8 % alignment-quality gap between best- and worst-served nations, and notes that native-language prompting actually widens this gap. Sparse autoencoder analysis identifies a candidate feature in the DPO stage that mediates suppression in the Tulu-3-8B model.

By Pardis Sadat Zahraei, Gokhan Tur, Dilek Hakkani-T\"ur, Ehsaneddin Asgari
arXiv AI
Sep 25

Cultural Divergence Preservation: Diagnosing Flattening and Caricature in LLM-Simulated Survey Populations

The paper introduces Cultural Divergence Preservation (CDP), a new diagnostic for evaluating whether large language models (LLMs) preserve cross‑country differences when used as synthetic survey respondents. CDP uses a single human calibration to detect cultural flattening (reduced divergence) or caricature (increased divergence) and is shown to vary monotonically with cross‑country divergence, unlike conventional Jensen–Shannon divergence metrics. Experiments across multiple LLM backbones, prompting methods, and survey domains reveal that CDP uncovers systematic discrepancies with traditional fidelity metrics, highlighting that methods favored by those metrics can still produce strong flattening.

By Yeeun Chae, Yewon Choi, Seunghyun Lee, IL Im