LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models
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.
67bc8aeb70194f240328e1cf
Hugo Pitorro
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2025-02-24T15:46:13.861Z
62d19a4b1e36881a57f31c6a
https://cdn-avatars.huggingface.co/v1/production/uploads/62d19a4b1e36881a57f31c6a/C-tAc0uXvpIggh0nWB2Dy.jpeg
Hugo Pitorro
user
twigs
67bc8aeb70194f240328e1d0
Marcos Treviso
LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models
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.
LaTIM provides token-level interpretability for Mamba SSMs, revealing their processing mechanisms across layers and enhancing understanding of sequence modeling.
state space models SSMs Mamba interpretability tools token-wise contributions token-level decomposition machine translation copying retrieval-based generation token-to-token interaction patterns