1

We explore how alignment impacts performance along three axes of global representation, English dialects, multilingualism, and opinions from and about countries worldwide. Our results show that current alignment procedures create disparities between English dialects and global opinions. We find alignment improves capabilities in several languages. We conclude by discussing design decisions that led to these unintended impacts and recommendations for more equitable preference tuning.

We release the MultiSim benchmark, a collection of 27 resources in 12 distinct languages containing over 1.7 million complex-simple sentence pairs. This benchmark will encourage research in developing more effective multilingual text simplification models and evaluation metrics. Our experiments using MultiSim with pre-trained multilingual language models reveal exciting performance improvements from multilingual training in non-English settings.

Litter can remain undetected and uncollected for extended periods of time, leading to detrimental consequences on the environment. The use of drones to detect this litter marks an important step towards solving this problem. We test five different computer vision algorithms for litter detection using drone surveillance and show a bagging ensemble of these methods to have the highest performance.