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