Belangrijkste concepten
The Mamba architecture, a recently proposed state space model, exhibits similar in-context learning (ICL) capabilities to transformer models, making it an efficient alternative for tasks involving long input sequences.
Samenvatting
This work investigates the in-context learning (ICL) capabilities of the Mamba architecture, a recently proposed state space model, and compares its performance to transformer models. The key findings are:
Mamba closely matches the ICL performance of transformer models on simple function approximation tasks, such as linear regression, sparse linear regression, 2-layer ReLU neural networks, and decision trees. Mamba outperforms its predecessor S4 and the RWKV model on these tasks.
The authors provide preliminary insights into the mechanism by which Mamba solves ICL tasks, finding that it employs an iterative optimization strategy similar to that of transformer models. This is demonstrated through a simple probing approach that analyzes the intermediate representations of the models.
On more complex natural language processing (NLP) tasks, the authors show that larger Mamba models (up to 2.8 billion parameters) achieve ICL performance on par with transformer-based language models like LLaMA, Pythia, and GPT-J, while outperforming the RWKV model.
Mamba's linear-time complexity for the forward pass, in contrast to the quadratic complexity of transformers, makes it a promising alternative for processing long input sequences in ICL tasks.
Overall, the results suggest that Mamba can be an efficient and performant option for in-context learning, particularly in applications involving long input sequences, and may enable generalizations of in-context learned AutoML algorithms to such settings.
Statistieken
The input dimension for the linear regression tasks is 20.
The number of in-context examples used for training is 40 for linear regression and 100 for ReLU neural networks and decision trees.
The authors tested the models on varying numbers of in-context examples, up to 160, to measure extrapolation performance.
Citaten
"Our results demonstrate that, across both categories of tasks, Mamba closely matches the performance of transformer models for ICL."
"Further analysis reveals that, like transformers, Mamba appears to solve ICL problems by incrementally optimizing its internal representations."