Long-form analogies generated by chatGPT lack human-like
  psycholinguistic properties
S. M. Seals Valerie L. Shalin
Psycholinguistic analyses provide a means of evaluating large language model
(LLM) output and making systematic comparisons to human-generated text. These
methods can be used to characterize the psycholinguistic properties of LLM
output and illustrate areas where LLMs fall short in comparison to
human-generated text. In this work, we apply psycholinguistic methods to
evaluate individual sentences from long-form analogies about biochemical
concepts. We compare analogies generated by human subjects enrolled in
introductory biochemistry courses to analogies generated by chatGPT. We perform
a supervised classification analysis using 78 features extracted from
Coh-metrix that analyze text cohesion, language, and readability (Graesser et.
al., 2004). Results illustrate high performance for classifying
student-generated and chatGPT-generated analogies. To evaluate which features
contribute most to model performance, we use a hierarchical clustering
approach. Results from this analysis illustrate several linguistic differences
between the two sources.