Papers with multilingual evaluation framework

2 papers
Semantic Evaluation of Multilingual Data-to-Text Generation via NLI Fine-Tuning: Precision, Recall and F1 scores (2025.findings-acl)

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Challenge: KG-to-Text models are prone to errors like Additions and Omissions, and few languages are taken into account since both train and test data are not readily available.
Approach: They propose a multilingual evaluation framework that is reference-less . it allows estimating how much a KG-to-Text Model under- (omission) or over- (addition) generates.
Outcome: The proposed evaluation framework outperforms prior reference-less metrics in correlation with human judgments and provides scores for precision and recall.
Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2025.acl-long)

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Challenge: Reliable multilingual evaluation is difficult and culturally appropriate evaluation is even harder to achieve.
Approach: They propose a multilingual evaluation framework that aims to mitigate these biases by improving translations and annotation practices.
Outcome: The proposed framework improves translation quality and cultural coverage and is culturally sensitive and culturally agnostic.

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