P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs

ACL ARR 2025 May Submission521 Authors

Abstract: Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. To alleviate this drawback, we aim to present a comprehensive multilingual multitask benchmark. First, we introduce P-MMEval, a large-scale benchmark covering fundamental and capability-specialized datasets. Furthermore, P-MMEval delivers consistent language coverage across various datasets and provides parallel samples. Finally, we conduct extensive experiments on representative multilingual model series to compare performances across models and tasks, explore the relationship between multilingual performances and factors such as tasks, model sizes, languages, and prompts, and examine the effectiveness of knowledge transfer from English to other languages. The resulting insights are intended to offer valuable guidance for future research.

Paper Type: Long

Research Area: Multilingualism and Cross-Lingual NLP

Research Area Keywords: multilingual benchmarks, multilingual evaluation

Contribution Types: Data resources

Languages Studied: English, Chinese, Arabic, Spanish, Japanese, Korean, Thai, French, Portuguese, Vietnamese

Submission Number: 521