Mamba Knockout for Unraveling Factual Information Flow
Nir Endy, Idan Daniel Grosbard, Yuval Ran-Milo, Yonatan Slutzky, Itay Tshuva, Raja Giryes

TL;DR
This paper explores how factual information flows within Mamba language models by adapting attention interpretability techniques, revealing both model-specific and universal patterns of information transmission across tokens and layers.
Contribution
It introduces a novel application of attentional interpretability to Mamba models, uncovering internal information dynamics and their relation to Transformer architectures.
Findings
Identifies patterns of information emergence and layer-wise dynamics.
Reveals both model-specific and universal information flow phenomena.
Disentangles feature roles in token exchange and enrichment.
Abstract
This paper investigates the flow of factual information in Mamba State-Space Model (SSM)-based language models. We rely on theoretical and empirical connections to Transformer-based architectures and their attention mechanisms. Exploiting this relationship, we adapt attentional interpretability techniques originally developed for Transformers--specifically, the Attention Knockout methodology--to both Mamba-1 and Mamba-2. Using them we trace how information is transmitted and localized across tokens and layers, revealing patterns of subject-token information emergence and layer-wise dynamics. Notably, some phenomena vary between mamba models and Transformer based models, while others appear universally across all models inspected--hinting that these may be inherent to LLMs in general. By further leveraging Mamba's structured factorization, we disentangle how distinct "features" either…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Network Security and Intrusion Detection
MethodsLinear Layer · Adam · Dense Connections · Softmax · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Label Smoothing · Multi-Head Attention · Mamba: Linear-Time Sequence Modeling with Selective State Spaces · Byte Pair Encoding
