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Unlocking the Secrets of Language Models
This chapter explores groundbreaking research by Anthropic on understanding large language models through techniques like Circuit Tracing and Chain of Tools. It reveals how these models utilize internal reasoning, challenges the simplistic view of LLMs, and emphasizes the significance of effective tool use and knowledge representation. The insights into hidden knowledge retention raise important questions about trust and interpretability in AI systems.