2 citations · 2 across the 1 of their papers we have counts for
2 papers
cs.CY2024
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…