Summary of Universal In-context Approximation by Prompting Fully Recurrent Models, By Aleksandar Petrov et al.
Universal In-Context Approximation By Prompting Fully Recurrent Models
by Aleksandar Petrov, Tom A. Lamb, Alasdair Paren, Philip H.S. Torr, Adel Bibi
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper investigates whether fully recurrent neural networks (RNNs), like RNNs, LSTMs, and SSMs, can serve as universal in-context approximators. While transformer models were previously shown to possess this property due to their attention mechanism, the authors demonstrate that RNN-based architectures, including Linear RNNs, GRUs, and linear gated architectures like Mamba and Hawk/Griffin, also have this capability. To facilitate analysis of these fully recurrent models, the paper introduces a programming language called LSRL, which compiles to these architectures. The study highlights the role of multiplicative gating in improving stability and practical viability for in-context universal approximation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores if certain types of neural networks can be taught to do any task without needing extra training. They found that some types of networks, like RNNs and LSTMs, can do this, even though they don’t have the same special attention mechanism as other networks. The authors also created a new way to write code called LSRL that helps analyze these types of networks. This study shows how these networks work better when they use a certain technique called multiplicative gating. |
Keywords
» Artificial intelligence » Attention » Rnn » Transformer
