Disclaimer: This podcast is completely AI generated by ⁠⁠⁠⁠⁠NoteBookLM⁠⁠⁠⁠⁠ 🤖

Summary

In this episode we discuss this research paper, which investigates whether transformer-based language models can learn to reason implicitly over knowledge, a skill that even the most advanced models struggle with. The authors focus on two types of reasoning: composition (combining facts) and comparison (comparing entities' attributes). Their experiments show that transformers can learn implicit reasoning, but only after extended training, a phenomenon known as grokking. The study then investigates the model's internal mechanisms during training to understand how and why grokking happens. The authors discover that transformers develop distinct circuits for composition and comparison, which explains the differences in their ability to generalise to unseen data. Finally, the paper demonstrates the power of parametric memory for complex reasoning tasks, showcasing a fully grokked transformer's superior performance compared to state-of-the-art LLMs that rely on non-parametric memory.