Summary of Mimetic Initialization Helps State Space Models Learn to Recall, by Asher Trockman et al.
Mimetic Initialization Helps State Space Models Learn to Recall
by Asher Trockman, Hrayr Harutyunyan, J. Zico Kolter, Sanjiv Kumar, Srinadh Bhojanapalli
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Recent advancements in state space models have demonstrated a significant performance gap compared to Transformers on recall-based tasks, attributed to the constant state size with respect to input sequence length. However, state space models often possess larger state sizes, leading us to hypothesize that they can perform better than previously reported. This paper investigates whether training difficulties contribute to their poor copying and recall performance rather than fundamental capacity constraints. By analyzing attention maps, we propose a structured initialization technique that enables state space layers to mimic attention, making it easier for Mamba to learn copy and associative recall from scratch across various architecture settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Research has shown that some types of models called “state space models” are not as good at certain tasks as another type of model called “Transformers.” But these state space models actually have a lot more information stored in them, which makes us wonder if they should be able to do better than they currently can. In this study, we look into why these state space models aren’t doing well and whether it’s because of the way they’re trained rather than their fundamental abilities. We also propose a new way to start training these models that allows them to learn more easily. |
Keywords
» Artificial intelligence » Attention » Recall
