On the "Induction Bias" in Sequence Models
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.