  Dictionary example sentences play an important role in illustrating word
definitions and usage, but manually creating quality sentences is challenging.
Prior works have demonstrated that language models can be trained to generate
example sentences. However, they relied on costly customized models and word
sense datasets for generation and evaluation of their work. Rapid advancements
in foundational models present the opportunity to create low-cost, zero-shot
methods for the generation and evaluation of dictionary example sentences. We
introduce a new automatic evaluation metric called OxfordEval that measures the
win-rate of generated sentences against existing Oxford Dictionary sentences.
OxfordEval shows high alignment with human judgments, enabling large-scale
automated quality evaluation. We experiment with various LLMs and
configurations to generate dictionary sentences across word classes. We
complement this with a novel approach of using masked language models to
identify and select sentences that best exemplify word meaning. The eventual
model, FM-MLM, achieves over 85.1% win rate against Oxford baseline sentences
according to OxfordEval, compared to 39.8% win rate for prior model-generated
sentences.
