arXiv (All)
open access
DiSCo: A Distribution-First Steering and Cultural Prior Evaluation Framework for Measuring Cultural Preference Bias in LLMs
Large language models (LLMs) are increasingly deployed in globally used assistants, yet their default choices in culturally grounded everyday situations can systematically favour some cultures over others, affecting localisation, user trust, and equitable behaviour. Existing cultural benchmarks eval
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Cultural Divergence Preservation: Diagnosing Flattening and Caricature in LLM-Simulated Survey Populations#1077999
- CRISP: Cultural Reward Modeling for Implicit Situated Propriety#1119418
- TOWARDS EXPLAINING, EVALUATING, AND MITIGATING CULTURAL BIAS IN LARGE LANGUAGE MODELS#637484
- Silicon sampling answers with country-level assumptions, not individual attitudes: Cross-national evidence from the European Social Survey#1110755
- Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions#1143156
Record · ID 997152
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive