ArticleBody:Large language models like those offered by OpenAI and Google famously require vast troves of training data to work. The latest versions of these models have already scoured much of the existing internet which has led some to fear there may not be enough new data left to train future iterations. Some prominent voices in the industry, like Meta CEO Mark Zuckerberg have posited a solution to that data dilemma: simply train new AI systems on old AI outputs.
In those last generations, the models trained on models are so far removed from the original training data that they begin to forget key aspects of the initial training and lose the plot entirely. It’s at this stage that models begin generating complete meaningless gibberish. When this happens, the researchers say the model’s “indiscriminate” self-cannibalizing of its own previous outputs “causes irreversible defects in the resulting model.