Analysis
This research reveals the fascinating geometric structures that emerge within a Large Language Model (LLM), showing how text-based models can implicitly grasp spatial and temporal concepts. The study highlights the role of word co-occurrence statistics and mathematical principles like Fourier basis in forming these intuitive representations, offering new insights into how LLMs understand the world.
Key Takeaways
- •LLMs can create circular and linear structures to represent cyclical and sequential data, respectively.
- •The geometric shapes in LLMs arise from the matrix decomposition of word co-occurrence data.
- •The study reveals how translation symmetry leads to Fourier bases in the LLM's internal representations.
Reference / Citation
View Original"The reason why such shapes are born is because the learning process of LLM is essentially performing 'matrix decomposition' of the co-occurrence matrix."