The continued usefulness of vocabulary tests for evaluating large language models

In their seminal article on semantic vectors, Landauer and Dumain (1997) proposed testing the quality of AI language models with a challenging vocabulary test. We show that their Test of English as a Foreign Language (TOEFL) test remains informative for contemporary major language models, since none of the models was perfect and made errors on divergent items. The TOEFL test consists of target words with four alternatives to choose from. We further tested the models on a Yes/No test that requires distinguishing between existing words and made-up nonwords. The models performed significantly worse on the nonword items, in line with other observations that current major language models provide non-existent information. The situation was worse when we generalized the tests to Spanish. Here, most models gave meanings/translations for the majority of random letter sequences. On the plus side, the best models began to perform quite well, and they also pointed to nonwords that were unknown to the test participants but can be found in dictionaries.