LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models
Hugo Pitorro, Marcos Treviso
2025-02-26
Summary
This paper talks about LaTIM, a new method that helps explain how Mamba models, a type of AI, process information by breaking down interactions between individual parts of the input data.
What's the problem?
Mamba models are becoming popular for tasks involving long sequences of data, but they lack tools to show how they make decisions. This makes it hard to understand or improve them, especially compared to other AI systems like transformers that already have better interpretability tools.
What's the solution?
The researchers created LaTIM, a method that analyzes how different parts of the input data (called tokens) interact with each other in Mamba models. It works for both Mamba-1 and Mamba-2 and allows detailed insights into how these models process information across layers. They tested LaTIM on tasks like translation and data retrieval to show how it reveals important patterns in the model's behavior.
Why it matters?
This matters because it makes Mamba models more transparent and easier to understand, which is crucial for improving their reliability in real-world applications. By providing tools to see how these models work internally, LaTIM could help developers build better AI systems for handling long and complex data sequences.
Abstract
State space models (SSMs), such as Mamba, have emerged as an efficient alternative to transformers for long-context sequence modeling. However, despite their growing adoption, SSMs lack the interpretability tools that have been crucial for understanding and improving attention-based architectures. While recent efforts provide insights into Mamba's internal mechanisms, they do not explicitly decompose token-wise contributions, leaving gaps in understanding how Mamba selectively processes sequences across layers. In this work, we introduce LaTIM, a novel token-level decomposition method for both Mamba-1 and Mamba-2 that enables fine-grained interpretability. We extensively evaluate our method across diverse tasks, including machine translation, copying, and retrieval-based generation, demonstrating its effectiveness in revealing Mamba's token-to-token interaction patterns.