Web: http://arxiv.org/abs/2205.06203

May 13, 2022, 1:11 a.m. | Antonio Laverghetta Jr., Animesh Nighojkar, Jamshidbek Mirzakhalov, John Licato

cs.CL updates on arXiv.org arxiv.org

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 …

arxiv computational human language language models models

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