The article also highlights an interesting phenomenon where LLMs, when asked to pick a number between 1 and 100, often choose 42, likely due to the number's overrepresentation in training data. The author concludes that LLMs are not suitable for discovering rare truths or valuable neglected information, and are not reliable for mission-critical systems. The author also warns of the potential dangers of LLMs, such as privacy invasion and social manipulation.
Key takeaways:
- Large Language Models (LLMs) do not perform reasoning over data in the way that most people conceive or desire. They do not self-reflect on their information and cannot differentiate between truth and hallucination.
- LLMs are not suitable for finding rare hidden truths or valuable neglected information. They tend to converge towards popular narrative or data and cannot invent new concepts or reveal rarely spoken about concepts.
- LLMs are not reliable for mission-critical systems that require deterministic, provably correct behavior. They can be impressively convincing when they are wrong, which may lead to ill-advised adoption.
- Ironically, LLMs are failing at the primary use cases that are attracting billions of investment, but are proficient at undesirable use cases such as destruction of privacy and liberty, social manipulation, and the severance of human connection.