Energy-Based Transformers as Predictors of Reading Difficulty
Transformer language models have become established tools for modeling human sentence processing, with measures such as surprisal and attention entropy serving as effective predictors of reading difficulty that together capture complementary aspects of processing load. Here, we explore a related class of transformer models: energy-based transformers, which provide a principled formal link to associative memory models, bringing processing research into direct contact with the broader literature on Hopfield networks and dense associative memory. To our knowledge, this is the first exploration of an energy-based transformer measure in computational psycholinguistics. Across reading-time corp...
| Authors | Jakub Dotlacil, Ece Takmaz |
|---|---|
| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 1 September 2026 |
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