Summary
In this chapter, we've embarked on an exploration of LLMs, diving into their historical background, current capabilities, and the common misconceptions that surround these powerful tools. This journey through the development of LLMs not only highlights the technological breakthroughs that have shaped these models but also points toward future advancements and the challenges that lie ahead.
LLMs use an auto-regressive method to predict the next word in a sequence by considering previous words, but this approach has limitations. For instance, the likelihood of errors increases as the sequence lengthens because each prediction carries a chance of error that accumulates over time. Despite their impressive fluency, LLMs cannot truly plan or understand context as humans do, often producing responses that are a mere recombination of learned data without real insight. This is due to their training being limited to existing text, which prevents them from generating novel content or...
