19 citations · 27 across the 3 of their papers we have counts for
4 papers
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
Uhura: A Benchmark for Evaluating Scientific Question Answering and Truthfulness in Low-Resource African Languages
Edward Bayes, Israel Abebe Azime, Jesujoba O. Alabi +11
Evaluations of Large Language Models (LLMs) on knowledge-intensive tasks and factual accuracy often focus on high-resource languages primarily because datasets for low-resource lan…
Case Studies of AI Policy Development in Africa
Kadijatou Diallo, Jonathan Smith, Chinasa T. Okolo +3
Artificial Intelligence (AI) requires new ways of evaluating national technology use and strategy for African nations. We conduct a survey of existing 'readiness' assessments both…
The Grand Illusion: The Myth of Software Portability and Implications for ML Progress
Fraser Mince, Dzung Dinh, Jonas Kgomo +2
Pushing the boundaries of machine learning often requires exploring different hardware and software combinations. However, the freedom to experiment across different tooling stacks…