大语言模型多语言摘要评估：英语与孟加拉语综合研究
原标题：Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali
AI 摘要
该研究在arXiv cs.CL发布，对八个闭源和开放权重的大语言模型在英语和孟加拉语XL-Sum摘要任务上进行零样本基准评测，结合词汇/语义指标、无参考事实性验证和LLM-as-a-Judge多维评估。结果显示跨语言性能差异显著：GPT-5.6 Luna在英语词汇指标领先，但孟加拉语中LLaMA 3.3 70B取得最高ROUGE和BLEU，Gemini 2.5 Flash获得最高孟加拉语事实性得分。不同评估方法产生不同模型排名，表明英语摘要优势不一定迁移到孟加拉语，低资源语言需综合评估框架。
正文节选
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali Abstract Abstractive summarization remains challenging across languages, where linguistic quality and factual consistency may diverge, particularly in low-resource settings. We present a standardized zero-shot benchmark of eight closed-source and open-weight LLMs on size-matched English and Bengali XL-Sum test sets. Our evaluation combines lexical and semantic metrics with re