Part 2/9:
Unlike conventional programming, where specific problems and logic are pre-coded, large language models are trained on vast datasets. During this training phase, the models learn to analyze and generate language based on the innumerable computations performed across millions of instances. It is crucial for researchers and developers to comprehend why these models produce specific outputs, not only out of curiosity but also for safety reasons. If a model delivers desired outputs without clarity on its processes, it could, in theory, be presenting falsehoods while seeming correct.