Inside Out: Uncovering How Comment Internalization Steers LLMs for Better or Worse

This article likely explores the impact of comment internalization on Large Language Models (LLMs). It suggests that the way LLMs process and incorporate comments (perhaps from training data or user interactions) significantly influences their performance and behavior. The research probably investigates both positive and negative consequences of this internalization process, potentially examining how it affects aspects like bias, accuracy, and overall model effectiveness.