Summary:
- This article discusses the importance of "chain-of-thought" in evaluating the monitorability of large language models (LLMs) like GPT-3.
- Chain-of-thought refers to the step-by-step reasoning process that an LLM uses to arrive at its final output, which can provide valuable insights into how the model is making decisions.
- Monitoring the chain-of-thought can help researchers and developers better understand the inner workings of LLMs, identify potential biases or errors, and improve the transparency and trustworthiness of these powerful AI systems.