The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we introduce **CLEME2.0**, a reference-based metric describing four fundamental aspects of GEC systems: hit-correction, wrong-correction, under-correction, and over-correction. They collectively contribute to exposing critical qualities and locating drawbacks of GEC systems. Evaluating systems by combining these aspects also leads to superior human consistency over other reference-based and reference-less metrics. Extensive experiments on two human judgment datasets and six reference datasets demonstrate the effectiveness and robustness of our method, achieving a new state-of-the-art result. Our codes are released at https://github.com/THUKElab/CLEME.

This paper presents our approach for SemEval-2025 Task 1, Advancing Multimodal Idiomaticity Representation (AdMIRe), which focuses on idiom image ranking via semantic similarity. We explore multiple strategies, including neural networks on extracted embeddings and Siamese networks with triplet loss. A key component of our methodology is the application of advanced prompt engineeringtechniques within multimodal in-context learning (ManyICL), leveraging GPT-4o, CLIP.Our experiments demonstrate that structured and optimized prompts significantly enhancethe model’s ability to interpret idiomatic expressions in a multimodal setting.