Is Generative AI Always Accurate?
Is generative AI always accurate? In a simple word, no. The large language models (LLMs) used in generative AI are known to "hallucinate," meaning they can produce entirely fictional answers and present them with confidence. But why does this happen, and could it get worse over time?
There are several reasons for these inaccuracies. Firstly, these models don't actually understand the real world. They rely on pattern matching to generate sentences, which means they can present complete falsehoods in a way that sounds convincing. LLMs generate patterns in text based on the data they've been trained on. If asked about something outside their training data, they might create plausible-sounding but incorrect responses.
Another reason for these inaccuracies is the type of data these models are trained on. Initially, models are trained on human-generated data, which can itself contain inaccuracies, leading to hallucinations in AI responses. Interestingly, many models are also trained on what's called "synthetic data." This is when a model is trained on the output of AI rather than just human data. According to a Financial Times article, this practice can result in more incorrect responses as the inaccuracies in the AI's output are amplified. The research mentioned in the article even suggests that over time, some models could collapse into gibberish as a result.
What Does This Mean for You?
Firstly, it's crucial to recognise that generative AI models can and do get things wrong. Always fact-check any output before acting on it by consulting trusted sources. If you’re researching a topic, use these models as a starting point, but delve deeper rather than taking their output at face value.
Secondly, as the availability of human-generated data declines and models rely more on synthetic data, hallucinations are likely to become more frequent. This means you’ll need to be even more vigilant about checking the accuracy of the information provided by AI.
