Roles of Scaling and Instruction Tuning in Language Perception: Model
  vs. Human Attention
Changjiang Gao Shujian Huang Jixing Li Jiajun Chen
Recent large language models (LLMs) have revealed strong abilities to
understand natural language. Since most of them share the same basic structure,
i.e. the transformer block, possible contributors to their success in the
training process are scaling and instruction tuning. However, how these factors
affect the models' language perception is unclear. This work compares the
self-attention of several existing LLMs (LLaMA, Alpaca and Vicuna) in different
sizes (7B, 13B, 30B, 65B), together with eye saccade, an aspect of human
reading attention, to assess the effect of scaling and instruction tuning on
language perception. Results show that scaling enhances the human resemblance
and improves the effective attention by reducing the trivial pattern reliance,
while instruction tuning does not. However, instruction tuning significantly
enhances the models' sensitivity to instructions. We also find that current
LLMs are consistently closer to non-native than native speakers in attention,
suggesting a sub-optimal language perception of all models. Our code and data
used in the analysis is available on GitHub.