Transformer models struggle with state tracking and data efficiency compared to RNNs

A new research paper published on arXiv explores the limitations of transformer-based language models in state tracking, a critical aspect for understanding sequential data. The study reveals that transformers require significantly more training data than recurrent neural networks (RNNs) to achieve similar performance, especially as state-space size and sequence length increase. Furthermore, transformers demonstrate poor weight sharing across different sequence lengths, suggesting they learn length-specific solutions in isolation, unlike RNNs which exhibit effective amortized learning. AI

IMPACT Highlights fundamental challenges in transformer architectures for state tracking, potentially guiding future model development towards more data-efficient and generalizable sequence processing.

RANK_REASON The cluster contains an academic paper detailing new research findings on model capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• MReza Ebrahimi
• Recurrent Neural Networks
• Transformer

• paper
• model release

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