Abstract
Cross-lingual summarization (CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at various English tasks. However, unlike languages such as English, Chinese or Spanish, for those relatively low-resource languages with limited usage or data, recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings. This raises the question: Are LLMs capable of handling cross-lingual summarization tasks for low-resource languages? To resolve this question, we fully explore the potential of large language models on cross-lingual summarization task for low-resource languages through our four-step zero-shot method: Summarization, Improvement, Translation and Refinement (SITR) with correspondingly designed prompts. We test our proposed method with multiple LLMs on two well-known cross-lingual summarization datasets with various low-resource target languages. The results show that: i) GPT-3.5 and GPT-4 significantly and consistently outperform other baselines when using our zero-shot SITR methods. ii) By employing our proposed method, we unlock the potential of LLMs, enabling them to effectively handle cross-lingual summarization tasks for relatively low-resource languages.
Abstract (translated)
跨语言摘要（CLS）的目标是用不同于源文本的语言生成一个摘要。当前，经过指令调优的大规模语言模型（LLMs）在各种英语任务中表现出色。然而，与英语、中文或西班牙语等语言不同的是，对于那些使用范围有限或数据较少的低资源语言，最近的研究表明，在少量样本设置下，LLMs 在 CLS 任务上的表现仍然不尽如人意。这引发了一个问题：大规模语言模型是否能够处理针对低资源语言的跨语言摘要任务？为了解决这个问题，我们通过一种四步零样本方法（总结、改进、翻译和优化[SITR]）及其相应设计的提示语，全面探索了大规模语言模型在面向低资源语言的跨语言摘要任务中的潜力。我们在两个著名的跨语言摘要数据集上测试了所提出的方法，并采用多种 LLM 进行实验，这些数据集包含各种低资源目标语言。结果显示：i) 当使用我们的零样本 SITR 方法时，GPT-3.5 和 GPT-4 与其它基线模型相比，表现显著且一致地更优。ii) 通过采用我们提出的方法，我们释放了 LLMs 的潜力，使其能够有效处理针对相对低资源语言的跨语言摘要任务。
URL
https://arxiv.org/abs/2410.20021
