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CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction
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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.
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Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
A new error-attribution dataset and fine-tuned judge model that outputs score, error category, and feedback for LLM responses.
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