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LATIM Sheds Light on Token Interactions within Mamba Models, Boosting Interpretability

State Space Models (SSMs) like Mamba are gaining traction as efficient alternatives to Transformers for processing long sequences, but they’ve lagged behind in interpretability. Now, researchers at the Instituto de Telecomunicações, Lisbon, have unveiled a novel method called LATIM (Measuring Latent Token-to-Token Interactions in Mamba Models) that significantly enhances our understanding of these models’ inner workings.

The core challenge lies in the fact that unlike Transformers, which use attention scores to directly visualize how the model prioritizes different parts of the input, Mamba lacks such an explicit mechanism. Existing methods attempt to bridge this gap by reformulating Mamba’s computations into attention-like representations, but these approaches often fall short in explicitly decomposing the contributions of individual tokens across different layers of the network.

LATIM fills this void by reformulating Mamba’s computation to enable a token-by-token analysis. Imagine you’re trying to understand how Mamba translates a sentence from English to French. LATIM allows you to see how each word in the English sentence influences the generation of each word in the French sentence, even across different layers of the model. This fine-grained analysis allows researchers to adapt existing attention-based interpretability techniques to Mamba architectures.

The researchers evaluated LATIM across a range of tasks, showcasing its effectiveness in revealing Mamba’s token interaction patterns. One key test was the “copying task,” where the model is tasked with repeating a given sequence. A well-performing model should create a diagonal interaction pattern between the original sequence and the copy, which LATIM highlighted much more clearly than previous methods. For instance, if the sequence starts with “hello,” the model should “attend” to that “hello” token when generating the new “hello” token as part of the copy.

LATIM was also tested on machine translation. Here, the model’s token alignment between the source and target language is critical for accurate translation. LATIM outperformed previous methods in capturing these alignments, providing a clearer picture of how the model maps words and phrases across languages.

The team further investigated Mamba’s performance on retrieval-based generation tasks. Here, Mamba needs to identify and extract specific information from a long context. By using a task called “Passkey Retrieval,” where the model is challenged to extract a hidden numeric value from distracting text, LATIM uncovered that Mamba sometimes struggles to maintain focus on the correct information when multiple potential answers are present. It may tend to focus on the first key, even if it is not the requested one.

“Our findings suggest that Mamba’s recall limitations in long-context tasks may stem from its sparse and decaying focus on relevant tokens,” the researchers wrote. This insight could guide future efforts to improve Mamba’s ability to handle complex retrieval tasks.

The researchers acknowledge that their method involves some approximation due to the non-linearity within Mamba’s architecture, and that further studies involving human evaluation would further validate LATIM’s effectiveness. However, LATIM represents a significant step forward in understanding Mamba models, paving the way for more transparent and reliable SSMs.