Analyzing the Effect of Linguistic Instructions on Paraphrase Generation

Abstract

Recent work has demonstrated that large language models can often generate fluent and linguistically correct text, adhering to given instructions. However, to what extent can they execute complex instructions requiring knowledge of fundamental linguistic concepts and elaborate semantic reasoning? Our study connects an established linguistic theory of paraphrasing with LLM-based practice to analyze which specific types of paraphrases LLMs can accurately produce and where they still struggle. To this end, we investigate a method of analyzing paraphrases generated by LLMs prompted with a comprehensive set of systematic linguistic instructions. We conduct a case study using GPT-4, which has shown strong performance across various language generation tasks, and we believe that other LLMs may face similar challenges in comparable scenarios. We examine GPT-4 from a linguistic perspective to explore its potential contributions to linguistic research regarding paraphrasing, systematically assessing how accurately the model generates paraphrases that adhere to specified transformation rules. Our results suggest that GPT-4 frequently prioritizes simple lexical or syntactic alternations, often disregarding the transformation guidelines if they overly complicate the primary task.