GROKKED LLM beats RAG Reasoning (Part 3)

In this video, the host delves into the intricacies of Grokking LLMs and compares their performance against traditional RAG (Retrieval-Augmented Generation) systems like GPT-4 Turbo and Gemini Pro 1.5. The video is part three of a series, focusing on the causal reasoning capabilities of Grokking LLMs post-Grokking phase transition.

The video begins by revisiting the concept of Grokking and its impact on LLMs. The host explains that Grokking enables LLMs to achieve near-perfect accuracy (close to 99%) on unseen tasks in development and test datasets. This phase transition is crucial for LLMs to reach their performance phase.

To provide context, the host discusses the geometric and hierarchical representation of semantic concepts in LLMs, referencing recent research papers. The canonical representation space and the concept of causal inner products are introduced to explain how LLMs encode complex semantic relationships.

The video then shifts focus to a specific task: comparison. The host sets up a configuration to test the Grokking LLM’s ability to compare attributes of entities. The results show that Grokking LLMs can achieve 100% accuracy in both in-distribution and out-of-distribution data for comparison tasks.

The host uses tools like Logic Lens and Causal Tracing to probe the layers of the transformer architecture, revealing how information is processed and stored at different layers. The findings indicate that Grokking LLMs store atomic facts in the lower layers and perform higher-order reasoning tasks in the upper layers.

A performance benchmark is then conducted to compare Grokking LLMs with GPT-4 Turbo and Gemini Pro 1.5 using a complex causal reasoning task. The results are striking: while GPT-4 Turbo and Gemini Pro 1.5 struggle with accuracy, Grokking LLMs achieve an impressive 99.3% accuracy.

The video concludes by highlighting the limitations of non-parametric memory-based models like RAG systems and emphasizing the potential of Grokking LLMs for deep reasoning tasks. The host suggests that combining parametric and non-parametric approaches may not be as effective as previously thought, given the superior performance of Grokking LLMs.

Overall, the video provides a comprehensive analysis of Grokking LLMs, demonstrating their advanced reasoning capabilities and potential to outperform traditional RAG systems in complex tasks.