"Mastering AI to Master the Future": How AI Will Impact Education and Inspire Interdisciplinary Research
- FOFA
- Apr 10
- 5 min read
Exploring Two Groundbreaking Papers on Circuit Tracing in Large Language Models
As artificial intelligence (AI) technology continues its rapid development, large language models (LLMs) such as Claude 3.5 Haiku have demonstrated remarkable capabilities, ranging from medical diagnosis to poetry composition. However, the "black box" nature of these models — their lack of transparency regarding internal operations — has become a significant challenge. This is especially critical as LLMs are increasingly applied in high-stakes domains where trust, transparency, and safety are paramount.
Two academic papers, Circuit Tracing: Revealing Computational Graphs in Language Models (link) and On the Biology of a Large Language Model (link), propose innovative "circuit tracing" methods to unveil the internal computational processes of LLMs. These approaches open new avenues for AI explainability research and have profound implications for the future of AI development. Below, we summarize the core findings of these papers and their significance for the future of AI.
