# Zero-Shot Cross-Lingual Summarization via Large Language Models

arXiv: https://arxiv.org/abs/2302.14229 · HTML: https://mlsanity.com/paper/2302.14229/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 69 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://mlsanity.com/paper/2302.10198/) (2023)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization](https://mlsanity.com/paper/2302.08081/) (2023)
- [NCLS: Neural Cross-Lingual Summarization](https://mlsanity.com/paper/1909.00156/) (2019)
- [MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs](https://mlsanity.com/paper/2104.08692/) (2021)
- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [Models and Datasets for Cross-Lingual Summarisation](https://mlsanity.com/paper/2202.09583/) (2022)
- [Cross-Lingual Abstractive Summarization with Limited Parallel Resources](https://mlsanity.com/paper/2105.13648/) (2021)
- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillation](https://mlsanity.com/paper/2112.03473/) (2021)
- [Mixed-Lingual Pre-training for Cross-lingual Summarization](https://mlsanity.com/paper/2010.08892/) (2020)

## Built on by (most cited first, 16 of 16)

- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering](https://mlsanity.com/paper/2402.11129/) (2024)
- [Towards Modeling Learner Performance with Large Language Models](https://mlsanity.com/paper/2403.14661/) (2024)
- [Towards Unifying Multi-Lingual and Cross-Lingual Summarization](https://mlsanity.com/paper/2305.09220/) (2023)
- [LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models](https://mlsanity.com/paper/2406.09008/) (2024)
- [Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model](https://mlsanity.com/paper/2404.16766/) (2024)
- [Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models](https://mlsanity.com/paper/2406.16135/) (2024)
- [Reasoning or Not? A Comprehensive Evaluation of Reasoning LLMs for Dialogue Summarization](https://mlsanity.com/paper/2507.02145/) (2025)
- [AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities](https://mlsanity.com/paper/2308.04992/) (2023)
- [Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes](https://mlsanity.com/paper/2405.06687/) (2024)
- [Enhancing Abstractive Summarization of Scientific Papers Using Structure Information](https://mlsanity.com/paper/2505.14179/) (2025)
- [Think out Loud: Emotion Deducing Explanation in Dialogues](https://mlsanity.com/paper/2406.04758/) (2024)
- [More is More: Addition Bias in Large Language Models](https://mlsanity.com/paper/2409.02569/) (2024)
- [Recent Trends in Unsupervised Summarization](https://mlsanity.com/paper/2305.11231/) (2023)
- [Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective](https://mlsanity.com/paper/2412.17787/) (2024)

## Abstract (arXiv)

Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language. Recently, the emergence of Large Language Models (LLMs), such as GPT-3.5, ChatGPT and GPT-4, has attracted wide attention from the computational linguistics community. However, it is not yet known the performance of LLMs on CLS. In this report, we empirically use various prompts to guide LLMs to perform zero-shot CLS from different paradigms (i.e., end-to-end and pipeline), and provide a preliminary evaluation on the generated summaries. We find that ChatGPT and GPT-4 originally prefer to produce lengthy summaries with detailed information. These two LLMs can further balance informativeness and conciseness with the help of an interactive prompt, significantly improving their CLS performance. Experimental results on three widely-used CLS datasets show that GPT-4 achieves state-of-the-art zero-shot CLS performance, and performs competitively compared with the fine-tuned mBART-50. Moreover, we also find some multi-lingual and bilingual LLMs (i.e., BLOOMZ, ChatGLM-6B, Vicuna-13B and ChatYuan) have limited zero-shot CLS ability. Due to the composite nature of CLS, which requires models to perform summarization and translation simultaneously, accomplishing this task in a zero-shot manner is even a challenge for LLMs. Therefore, we sincerely hope and recommend future LLM research could use CLS as a testbed.