Paper analyzes transformer expressive power using circuit complexity

A new paper explores the expressive power of transformers, a core component in modern large language models (LLMs). The research frames transformer capabilities by comparing them to established models of computation, particularly using concepts from circuit complexity. This approach allows for precise calibration of what transformers can achieve as language recognizers by relating their resource usage, such as attention and precision, to circuit parameters like gate types, size, and depth. AI

IMPACT Provides a theoretical framework for understanding the capabilities and limitations of transformer-based LLMs.

RANK_REASON The cluster contains a research paper published on arXiv discussing theoretical computer science concepts related to AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• attention
• Circuit complexity
• Circuits
• Hugging Face
• large-language models
• Models of Computation in Context - 7th Conference on Computability in Europe, CiE 2011, Sofia, Bulgaria, June 27 - July 2, 2011
• theoretical computer science
• transformers

• paper
• model release

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