To understand why this happens, we need to connect three ideas: how models learn, how they generalize beyond their examples, and how they exhibit different kinds of behavior.
In this article, we are going to understand how this connection works and why a model needs help understanding the order of the words even when the words already arrive in an ordered list.
In this article, we'll learn some key strategies that can help a developer make the right boundary-related decisions for the systems they are working on.
When LLMs sometimes agree with incorrect claims, it is mostly because their training rewards such behaviour. This reward system is built on several things at once, such as accuracy, helpfulness, politeness, and responses that people like.