Vassilina stated that data quality and availability taken from religious texts run the risk that translation quality contains biases and a more religious style in its translated texts.
“We can often observe cases of ‘hallucinations’ for such languages when the model generates the text that has nothing to do with the original input text. This implies that such systems should be used with a lot of caution. We also observed that low-resource languages are the first to suffer from quality decreases when we start to compress them for more hong kong mobile database efficient usage. (eg. quantization, pruning, distillation) could potentially impact low-resource languages and whether it would increase bias already present even further. Developing compression techniques that could take such considerations into account would definitely be beneficial for low-resource languages,” Vassilina stated.
As for Surangika, she explained that low-resource languages often come from developing countries. She points out that these countries may not have established data protection laws. It can also run the risk of worker exploitation, wherein people employed to gather and collect data are paid low wages. However, she believes this can be regulated by the government, and policies can be passed to prevent such exploitations and protect the local data. She also stated that researchers should be mindful as to “not to exploit these loopholes” or lack of regulations on these matters.