Beyond Static and Linear: What Attention Constraints Best Fit Human Reading Times?

arXiv:2608.23818 · cs.CL · Submitted 2026-08-24 · Read on arXiv

cs.CL

Submitted: 2026-08-24

Updated: 2026-10-23

Code: https://github.com/Lanni-ni/different-attention-mechanisms-transformers

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 32× in this paper · explained in 2 episodes
• Attention Mechanism — 13× in this paper · explained in 2 episodes
• Perplexity — 9× in this paper · explained in 3 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Artificial Neural Network — 1× in this paper
• Generalizability — 1× in this paper · explained in 6 episodes
• Memory Decay — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• The Pile: An 800GB Dataset of Diverse Text for Language Modeling
• Dual Alignment Between Language Model Layers and Human Sentence Processing
• Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity
• OPT: Open Pre-trained Transformer Language Models

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