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% in F-score and achieves comparable results with GPT-4 on ProLex.

翻译：词汇替换任务旨在为给定上下文句子中的目标词寻找合适的替代词。然而，该任务未能考虑那些语言能力水平与目标词相当或更高的替代词，而这一方面对于希望提升写作能力的语言学习者可能大有裨益。为弥补这一不足，我们提出了一项新任务：面向语言能力提升的词汇替换。同时，我们引入了ProLex，这是一个新颖的基准，旨在评估系统不仅生成合适替代词，还能生成展现更佳语言能力的替代词的能力。除了该基准外，我们还提出了能够自动执行此新任务的模型。实验表明，我们使用任务特定合成数据微调的Llama2-13B最佳模型，其F值平均优于ChatGPT达3.2%，并在ProLex上取得了与GPT-4相当的结果。