  This study analyzes changes in the attention mechanisms of large language
models (LLMs) when used to understand natural conversations between humans
(human-human). We analyze three use cases of LLMs: interactions over web
content, code, and mathematical texts. By analyzing attention distance,
dispersion, and interdependency across these domains, we highlight the unique
challenges posed by conversational data. Notably, conversations require nuanced
handling of long-term contextual relationships and exhibit higher complexity
through their attention patterns. Our findings reveal that while language
models exhibit domain-specific attention behaviors, there is a significant gap
in their ability to specialize in human conversations. Through detailed
attention entropy analysis and t-SNE visualizations, we demonstrate the need
for models trained with a diverse array of high-quality conversational data to
enhance understanding and generation of human-like dialogue. This research
highlights the importance of domain specialization in language models and
suggests pathways for future advancement in modeling human conversational
nuances.
