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

• URL: http://arxiv.org/abs/2406.04630v1
• Date: Fri, 7 Jun 2024 04:31:41 GMT
• Title: Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models
• Authors: Gyutae Park, Seojin Hwang, Hwanhee Lee,
• Abstract summary: Cross-lingual summarization (XLS) aims to generate a summary in a target language different from the source language document. Large language models (LLMs) have shown promising zero-shot XLS performance, their few-shot capabilities on this task remain unexplored. We investigate the few-shot XLS performance of various models, including Mistral-7B-Instruct-v0.2, GPT-3.5, and GPT-4.
• Score: 4.9325206373289125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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