Understanding what LLMs can and cannot do.
Modern Large Language Models possess a range of impressive capabilities that have captured the public imagination. One of their most powerful abilities is in-context learning, which includes zero-shot and few-shot learning. This means they can perform tasks they were not explicitly trained on, simply by being given a natural language prompt describing the task (zero-shot) or a prompt that includes a few examples (few-shot). This flexibility makes them incredibly versatile. They excel at generating coherent, contextually relevant, and stylistically varied text, making them useful for content creation, summarization, and brainstorming. They also have a vast amount of factual knowledge encoded within their parameters, allowing them to function as powerful question-answering systems. However, it's crucial to understand their limitations. A primary issue is 'hallucination,' the tendency to generate text that is plausible-sounding but factually incorrect or nonsensical. Because LLMs are probabilistic text generators, not databases, they don't have a concept of 'truth'; they only know what words are likely to follow other words. They can also inherit and amplify biases present in their training data, leading to unfair or stereotypical outputs. They lack true common-sense reasoning and can make simple logical errors that a human would not. Furthermore, their knowledge is static and limited to the point in time when their training data was collected; they are not aware of events that occurred after their training cutoff. Finally, their reasoning process is opaque, making it difficult to understand why they produce a particular output, which is a major challenge for applications requiring reliability and trust.