Transformer-based language models (LMs) continue to achieve state-of-the-art
performance on natural language processing (NLP) benchmarks, including tasks
designed to mimic human-inspired "commonsense" competencies. To better
understand the degree to which LMs can be said to have certain linguistic
reasoning skills, researchers are beginning to adapt the tools and concepts
from psychometrics. But to what extent can benefits flow in the other
direction? In other words, can LMs be of use in predicting the psychometric
properties of test items, when those items are given to human participants? If
so, the benefit for psychometric practitioners is enormous, as it can reduce
the need for multiple rounds of empirical testing. We gather responses from
numerous human participants and LMs (transformer- and non-transformer-based) on
a broad diagnostic test of linguistic competencies. We then use the human
responses to calculate standard psychometric properties of the items in the
diagnostic test, using the human responses and the LM responses separately. We
then determine how well these two sets of predictions correlate. We find that
transformer-based LMs predict the human psychometric data consistently well
across most categories, suggesting that they can be used to gather human-like
psychometric data without the need for extensive human trials.

本研究得出结论，Transformer-based language models 可以在模拟语言理解任务中预测人类心理测量数据，这为心理测量学家节省了多轮实验测试的必要性。