Challenges and limitations of generative AI
Though generative AI has immense benefits, it is not without its own set of challenges and limitations. Some of these challenges and limitations need to be taken into account with a lot of caution while considering a generative AI technology for any particular use case. Here’s a brief discussion of some of the most common caveats related to LLMs and some of the ways to mitigate them.
Quality of data and bias
The generative models are largely aided by the quality and diversity of data in the training dataset. Any model trained with biased or unrepresentative data will reproduce outputs with the same kind of bias, hence solidifying existing biases or allowing marginalization of one or several groups in case of bias in the training data.
One way this challenge can be dealt with is by ensuring richness in diversity, good quality data, and so on for a wide array of perspectives within the dataset itself used for training. As with...
