Filtering Mechanisms Shape Reasoning and Diversity in LLMs

This ArXiv paper highlights the impact of filtering on the reasoning capabilities and diversity of Large Language Models. Understanding these internal mechanisms is crucial for improving LLM performance and mitigating biases.

• •Filtering processes within LLMs significantly impact reasoning.
• •Diversity of outputs is directly influenced by filtering choices.
• •Further research is needed to refine filtering and reduce bias.