Is Mamba Capable of In-Context Learning? - An Analysis of State Space Models

Explore groundbreaking research in a 44-minute AutoML seminar examining Mamba's capabilities for in-context learning (ICL), presented by researchers Riccardo Grazzi and Julien Siems. Dive into empirical evidence demonstrating how Mamba, a state space model designed for better scaling with input sequence length, performs comparably to transformer models in ICL tasks. Learn about the evaluation process across simple function approximation and complex natural language processing problems, discovering how Mamba optimizes internal representations similarly to transformers. Understand the implications for meta-learning and potential applications in AutoML algorithms, particularly for handling long input sequences. Gain insights from the comprehensive analysis that positions Mamba as an efficient alternative to transformers for ICL tasks, supported by the research detailed in the accompanying paper available on arXiv.