Papers by Linlin Song

2 papers
CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction (2025.acl-long)

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Challenge: Existing studies have focused on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, but the interpretabilty of these metrics has been neglected.
Approach: They propose a reference-based metric that describes four aspects of GEC systems: hit-correction, wrong-corrections, under-correcties, and over-corrects.
Outcome: The proposed metric reveals critical qualities and locates drawbacks of GEC systems.
Diagnosing Failures in Large Language Models’ Answers: Integrating Error Attribution into Evaluation Framework (2025.findings-acl)

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Challenge: Existing evaluation models lack error attribution capability due to their proprietary nature.
Approach: They propose a misattribution framework with 6 primary and 15 secondary categories to facilitate in-depth analysis.
Outcome: The proposed framework is based on a dataset specifically designed for error attribution, along with the corresponding scores and feedback.

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