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This study demonstrates that current large language models capture next-word prediction but fail to account for the structural integration effort humans experience during complex sentence processing. The findings highlight a significant gap between probabilistic word forecasting and the cognitive mechanisms required to resolve syntactic ambiguity, challenging existing frameworks in natural language understanding research. This dissociation suggests future AI systems must evolve beyond simple probability estimation to better model human-like comprehension of context-dependent meaning.
Eye movements separate prediction from structural difficulty

Researchers found that analyzing eye movements reveals how prediction differs from structural difficulty in reading, using data from hundreds of participants.
Recordings of eye movements from hundreds of participants are revealing a surprising disconnect between how easily we anticipate words while reading and the actual difficulty of understanding sentence structure. Researchers led by William Timkey of Dell, University of Illinois at Urbana-Champaign found that while language models accurately predict upcoming words, they fail to explain the increased effort, shown through backward eye movements and rereading, needed to integrate those words into…
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