Large language models (LLMs) like ChatGPT are impressive, but they sometimes 'hallucinate,' meaning they generate incorrect or nonsensical information. Why does this happen? New research from the National University of Defense Technology in China explores this by looking at the inner workings of LLMs, specifically the 'self-attention' mechanism. Self-attention is how these models weigh different parts of a text to understand relationships between words. The researchers used a causal approach, essentially tweaking the self-attention layers within several open-source LLMs. Imagine turning different knobs inside the AI's brain and observing how it affects the output. They found that disabling certain self-attention layers, especially those at the beginning or end of the model's processing chain, actually reduced hallucinations! This suggests these layers are more susceptible to generating false information. Conversely, disabling layers in the middle of the processing chain often worsened the hallucinations, implying that these layers might be crucial for maintaining factual accuracy. This research provides valuable insight into why AI sometimes makes things up. It also hints at potential ways to mitigate these hallucinations by focusing on how the model's internal attention mechanisms are structured and trained. While a complete solution remains elusive, this work offers a promising new direction for understanding and, ultimately, controlling AI's tendency to hallucinate.