Bias in LLMs
In the world of AI, we’ve seen a boom in the deployment of LLMs, and hey – why not? These behemoths, such as GPT-3 or BERT, are capable of some jaw-dropping tasks, from writing emails that make sense to creating near-human-like text. Impressive, isn’t it? But let’s take a step back and think. Just like every coin has two sides, there’s a not-so-glamorous side to these models – bias.
Yes – you heard it right. These models are not immune to biases. The ugly truth is that these models learn everything from the data they’re trained on. And if that data has biases (which, unfortunately, is often the case), the model’s output can also be biased. Think of it this way: if the model were trained on texts that are predominantly sexist or racist, it might end up generating content that reflects these biases. Not a pleasant thought, is it?
And that’s not just a hypothetical scenario. There have been instances...
