Transformers have become the gold standard for understanding and generating sequences, while Generalized State Space Models (GSSMs) offer computational efficiency. Researchers have compared these models, showing that transformers outshine GSSMs in tasks requiring sequence replication. Their dynamic memory capacity enables them to handle memory-intensive operations, unlike GSSMs with fixed-size latent states. This study suggests exploring hybrid models combining computational efficiency with dynamic memory capabilities. [49 words]
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Transformers vs. Generalized State Space Models: Unveiling the Efficiency and Limitations in Sequence Modeling
Developing models capable of understanding and generating sequences has become crucial for progress. Transformers have emerged as a gold standard for capturing language intricacies with unparalleled precision. However, the rise of generalized state space models (GSSMs) has sparked a debate on their capability relative to transformers.
Key Findings:
- Transformers outshine GSSMs in sequence modeling tasks, especially in sequence replication and information retrieval.
- GSSMs have inherent limitations due to their fixed-size latent state, highlighting the architectural strengths of transformers in handling memory-intensive operations.
- The study paves the way for future research into hybrid models combining GSSMs’ efficiency with transformers’ dynamic memory capabilities.
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