Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models

Gyutae Park; Seojin Hwang; Hwanhee Lee

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arXiv · 2406.04630

Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models

Gyutae Park·Seojin Hwang·Hwanhee Lee

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Abstract

Cross-lingual summarization (XLS) aims to generate a summary in a target language different from the source language document. While large language models (LLMs) have shown promising zero-shot XLS performance, their few-shot capabilities on this task remain unexplored, especially for low-resource languages with limited parallel data. In this paper, we investigate the few-shot XLS performance of various models, including Mistral-7B-Instruct-v0.2, GPT-3.5, and GPT-4. Our experiments demonstrate that few-shot learning significantly improves the XLS performance of LLMs, particularly GPT-3.5 and GPT-4, in low-resource settings. However, the open-source model Mistral-7B-Instruct-v0.2 struggles to adapt effectively to the XLS task with limited examples. Our findings highlight the potential of few-shot learning for improving XLS performance and the need for further research in designing LLM architectures and pre-training objectives tailored for this task. We provide a future work direction to explore more effective few-shot learning strategies and to investigate the transfer learning capabilities of LLMs for cross-lingual summarization.

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BibTeX RIS

Gyutae Park, Seojin Hwang, Hwanhee Lee. 2024-06-07. Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models. https://arxiv.org/abs/2406.04630

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