Paraphrase-Augmented Evaluation for Dialogue Models: A Unified Framework with AMR and GPT-Based Rewriting

In recent years, large language models (LLMs) have achieved remarkable progress in dialogue generation. However, existing automatic evaluation methods still face challenges in diverse scenarios, particularly in terms of limited generalization and low alignment with human judgment. To address these issues, we propose a paraphrase-based evaluation framework that integrates Abstract Meaning Representation (AMR) with general-purpose language models (GPT). This approach generates diverse paraphrases across lexical, syntactic and stylistic dimensions to enhance the coverage of traditional evaluation metrics. Experimental results show that incorporating paraphrase augmentation significantly improves the correlation between automatic metrics and human evaluation on multiple datasets. Additionally, extensive experiments on six mainstream LLMs demonstrate the effectiveness and generalizability of the proposed method. This study offers new insights into improving the human alignment of automatic evaluation and lays a foundation for the application and optimization of LLMs in open-domain dialogue systems.