Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring

Understand

Large language models (LLMs) are becoming increasingly capable, but the mechanisms of their thinking and decision-making processes remain unclear.

• Chain-of-thoughts (CoTs) have been commonly utilized to externalize LLMs' thinking, but this strategy fails to accurately reflect LLMs' thinking process.
• Techniques based on LLMs' hidden representations provide an inner perspective to improve the monitorability of their latent thinking.
• However, previous methods only try to develop external modules instead of making LLMs themselves easier to monitor.