A Benchmark Dataset and Instruction Fine-Tuning Methods for Metaphorical Comprehension and Explanation

Fine-tuning large language models (LLMs) with instruction sets has become an effective method to improve the performance of LLMs. However, current metaphor datasets suffer from issues such as in-consistent annotation methods, insufficient data, limited task diversity, and a narrow genre scope, making it difficult to form a unified instruction set. Additionally, existing fine-tuning strategies have not considered the logical sequence and difficulty differences among metaphor tasks. To address these issues, we have released the “Metaphor Understanding and Generation Instruction Fine-Tuning Dataset” (MetaIFD). This dataset contains 113,384 high-quality entries from over ten genres and supports five metaphor tasks and sentiment analysis tasks. Furthermore, MetaIFD introduces two key annotations: the metaphor identification analysis process and sentiment labels. In this paper, we also propose a three-stage fine-tuning strategy, following the principles of “explicit sentiment tasks preceding implicit sentiment tasks” and “understanding tasks preceding generation tasks,” along with a difficulty-based training approach that considers the logical sequence and difficulty differences between tasks. Experimental results show that the fine-tuned model significantly improves performance across all tasks. The dataset and instruction set are publicly available at: https://github.com/DUTIR-YSQ/MetaIFD .