The generation vs. verification delta explains why LLM's are useful (simianwords.bearblog.dev)

A recent discussion highlights the "generation vs. verification delta" in understanding the utility of large language models (LLMs), such as ChatGPT. The argument posits that while LLMs require verification of their outputs, this does not diminish their usefulness. The key takeaway is that if LLMs can generate results that are largely directionally accurate, the effort needed for users to verify these outputs becomes minimal compared to the complexity of the initial generation. For example, when searching for a specific word like "confers," the ease of confirming the LLM's output illustrates its effectiveness, as users can quickly assess the accuracy. This concept has significant implications for the AI/ML community, as it suggests that LLMs can enhance productivity even if they occasionally make mistakes. Rather than demanding perfect accuracy, the focus shifts to achieving sufficient directional accuracy for practical use across various domains. The discussion encourages a reevaluation of how LLM-generated content is perceived and leveraged, particularly in fields where the verification process is straightforward, underscoring the evolving role of AI in collaborative tasks.