ProLex: A Benchmark for Language Proficiency-oriented Lexical Substitution
CoRR(2024)
摘要
Lexical Substitution discovers appropriate substitutes for a given target
word in a context sentence. However, the task fails to consider substitutes
that are of equal or higher proficiency than the target, an aspect that could
be beneficial for language learners looking to improve their writing. To bridge
this gap, we propose a new task, language proficiency-oriented lexical
substitution. We also introduce ProLex, a novel benchmark designed to assess
systems' ability to generate not only appropriate substitutes but also
substitutes that demonstrate better language proficiency. Besides the
benchmark, we propose models that can automatically perform the new task. We
show that our best model, a Llama2-13B model fine-tuned with task-specific
synthetic data, outperforms ChatGPT by an average of 3.2
achieves comparable results with GPT-4 on ProLex.
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