Long-form analogies generated by chatGPT lack human-like psycholinguistic properties
This work addresses the problem of evaluating LLM output quality for educational or linguistic applications, but it is incremental as it applies existing methods to a new dataset.
The study applied psycholinguistic methods to compare long-form analogies generated by human students and ChatGPT, finding high classification performance with 78 features, revealing several linguistic differences between the two sources.
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.