One of the most well-known issues with LLMs is the phenomenon of “hallucinations”, where it makes up facts and presents them confidently as true. It’s one of the main challenges of using LLMs in corporations, since improper validation by the user may result in people drawing conclusions based on flawed information (like this famous example, and this one). If it wasn’t obvious already not to blindly trust anything you find on the internet (on an app for that matter), even if it comes from fancy models most users hardly understand, it does highlight again that it’s important to be smart about how you ask questions and to validate the output from LLMs.
This page cannot be shown here. You can still read it on the original site — the toolbar below keeps your place in the directory.
One of the most well-known issues with LLMs is the phenomenon of “hallucinations”, where it makes up facts and presents them confidently as true. It’s one of the main challenges of using LLMs in corporations, since improper validation by the user may result in people drawing conclusions based on flawed information (like this famous example, and this one ). If it wasn’t…
Read on /posts/querying-databases-using-langchain-and-ollama/ ↗
Comments
Nothing yet. Say the first thing.
Sign in to join the conversation.