A webinar presenting fresh data on how young adults aged 22 are faring in terms of poverty, employment, education, living arrangements, and access to public benefits.
The team examined how AI, specifically LLMs, could streamline the case review process for SNAP applications to alleviate the burden on case workers while potentially improving accuracy.
This paper discusses the country’s chronic underinvestment in children and resulting outcomes, including new data on poverty rates among young children, is inextricable from the prospects of young children; and the remarkably comprehensive pandemic-era response policies, including which changes contributed most to reducing child poverty.
The team explored using LLMs to interpret the Program Operations Manual System (POMS) into plain language logic models and flowcharts as educational resources for SSI and SSDI eligibility, benchmarking LLMs in RAG methods for reliability in answering queries and providing useful instructions to users.
