Summary of The Illusion Of State in State-space Models, by William Merrill and Jackson Petty and Ashish Sabharwal
The Illusion of State in State-Space Models
by William Merrill, Jackson Petty, Ashish Sabharwal
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL)
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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 State-space models (SSMs) are being explored as an alternative architecture for building large language models (LLMs), potentially offering advantages over the traditional transformer architecture. SSMs are theoretically well-suited for sequential computation and state tracking, thanks to their similarity to recurrent neural networks (RNNs). However, this study reveals that SSMs do not actually have a significant advantage in expressive power for state tracking compared to transformers. The analysis shows that both SSMs and transformers are limited by the complexity class TC^0, making it impossible to solve simple state-tracking problems like permutation composition or accurately track chess moves with certain notation. Experimental results confirm that Mamba-style SSMs struggle with state tracking. This study highlights the limitations of SSMs in solving real-world state-tracking problems, suggesting that their “state” is an illusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine building super smart language models that can understand and process large amounts of text. Researchers are exploring a new way to do this called state-space models (SSMs). Some people think SSMs might be better than the current method, transformers, at understanding patterns in language and keeping track of what’s happening over time. But a new study shows that both SSMs and transformers have the same limitations when it comes to tracking changes over time. This means they can’t accurately keep track of complex events like chess moves or code evaluation. The researchers even ran experiments to confirm this, showing that SSMs struggle with keeping track of state. Overall, this study suggests that while SSMs are an interesting idea, they might not be the magic solution we’re looking for. |
Keywords
* Artificial intelligence * Tracking * Transformer
