Mind the (Language) Gap: Mapping the Challenges of LLM Development in Low-Resource Language Contexts
This white paper maps the LLM development landscape for low-resource languages, highlighting challenges, trade-offs, and strategies to increase investment; prioritize cross-disciplinary, community-driven development; and ensure fair data ownership.
In this response to a request for information issued by the National Science Foundation’s Networking and Information Technology Research and Development National Coordination Office (on behalf of the Office of Science and Technology Policy), scholars from Stanford HAI, CRFM, and RegLab urge policymakers to prioritize four areas of policy action in their AI Action Plan: 1) Promote open innovation as a strategic advantage for U.S. competitiveness; 2) Maintain U.S. AI leadership by promoting scientific innovation; 3) Craft evidence-based AI policy that protects Americans without stifling innovation; 4) Empower government leaders with resources and technical expertise to ensure a “whole-of-government” approach to AI governance.
This white paper assesses federal efforts to advance leadership on AI innovation and governance through recent executive actions and emphasizes the need for senior-level leadership to achieve a whole-of-government approach.
