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Retrievit: In-context Retrieval Capabilities of Transformers, State Space Models, and Hybrid Architectures
arXiv – CS AI|Georgios Pantazopoulos, Malvina Nikandrou, Ioannis Konstas, Alessandro Suglia||1 views
🤖AI Summary
Research compares Transformers, State Space Models (SSMs), and hybrid architectures for in-context retrieval tasks, finding hybrid models excel at information-dense retrieval while Transformers remain superior for position-based tasks. SSM-based models develop unique locality-aware embeddings that create interpretable positional structures, explaining their specific strengths and limitations.
Key Takeaways
- →Hybrid architectures combining Transformers and SSMs outperform pure SSMs and match Transformers in data efficiency for information-dense retrieval tasks.
- →Transformers maintain superiority in position retrieval tasks requiring two-hop associative lookups.
- →SSM-based models develop locality-aware embeddings where adjacent positions become neighbors in embedding space.
- →The research provides principled guidance for selecting architectures based on specific retrieval task requirements.
- →Fundamental differences exist in how Transformers versus SSMs learn positional associations and representations.
#transformers#state-space-models#hybrid-architectures#in-context-learning#retrieval-tasks#machine-learning#ai-research#model-efficiency#positional-embeddings
Read Original →via arXiv – CS AI
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