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.- Anthology ID:
- 2024.loresmt-1.6
- Volume:
- Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Atul Kr. Ojha, Chao-hong Liu, Ekaterina Vylomova, Flammie Pirinen, Jade Abbott, Jonathan Washington, Nathaniel Oco, Valentin Malykh, Varvara Logacheva, Xiaobing Zhao
- Venues:
- LoResMT | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 57–63
- Language:
- URL:
- https://aclanthology.org/2024.loresmt-1.6
- DOI:
- Cite (ACL):
- Gyutae Park, Seojin Hwang, and Hwanhee Lee. 2024. Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models. In Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024), pages 57–63, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models (Park et al., LoResMT-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.loresmt-1.6.pdf