Exploring challenges with LLMs

Not all the news is good, however. It’s time to also discuss the darker side of LLMs.

These models do have important limitations and some collateral effects too. Here is a list of the most important ones, but please consider it non-exhaustive. There may be others not included here, and the order is arbitrarily chosen:

• They lack access to real-time data.
  • LLMs are trained on a static dataset, meaning that the information they have is only as up to date as the data they were trained on, which might not include the latest news, scientific discoveries, or social trends.
  • This limitation can be critical when users seek real-time or recent information, as the LLMs might provide outdated or irrelevant responses. Furthermore, even if they cite data or statistics, these numbers are likely to have changed or evolved, leading to potential misinformation.

Note

While recent features introduced by OpenAI, for example, allow the underlying LLM...