2 citations · 4 across the 4 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
cs.CY2024★ 1 cited
The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation
Jabez Magomere, Shu Ishida, Tejumade Afonja +11
We introduce the World Wide recipe, which sets forth a framework for culturally aware and participatory data collection, and the resultant regionally diverse World Wide Dishes eval…
cs.SE2024★ 2 cited
LangProp: A code optimization framework using Large Language Models applied to driving
Shu Ishida, Gianluca Corrado, George Fedoseev +5
We propose LangProp, a framework for iteratively optimizing code generated by large language models (LLMs), in both supervised and reinforcement learning settings. While LLMs can g…