We’ve always thought large language models (LLMs) like Claude, GPT-4, and Gemini were just next-word predictors—but new research from Anthropic tells a very different story. In this video, I break down their blog post “Tracing the Thoughts of a Large Language Model” and explore what’s really happening under the hood.
LINK:
https://www.anthropic.com/news/tracing-thoughts-language-model
https://www.anthropic.com/research/auditing-hidden-objectives
https://arxiv.org/pdf/2503.21934v1
https://openai.com/index/chain-of-thought-monitoring/
https://epoch.ai/frontiermath/the-benchmark
https://www.lesswrong.com/posts/8ZgLYwBmB3vLavjKE/some-lessons-from-the-openai-frontiermath-debacle
https://transformer-circuits.pub/2025/attribution-graphs/biology.html
https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poems
https://bbycroft.net/llm
RAG Beyond Basics Course:
https://prompt-s-site.thinkific.com/courses/rag
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00:00 Introduction to LLMs
00:58 Understanding LLM Training
02:05 Exploring Multilingual Capabilities
02:49 Next Word Prediction vs. Planning Ahead
03:28 Interpreting LLM Reasoning
04:14 Comparing LLMs to Computer Vision Models
05:20 The Biology of a Large Language Model
05:53 Universal Language of Thought
12:00 LLMs and Mathematical Reasoning
15:23 Faithfulness in Chain of Thought
20:05 LLMs and Hallucinations
22:06 Understanding Jailbreaks