Summary of Think Carefully and Check Again! Meta-generation Unlocking Llms For Low-resource Cross-lingual Summarization, by Zhecheng Li et al.
Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization
by Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai, Naifan Cheung, Nanyun Peng, Kai-wei Chang
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the capability of large language models (LLMs) in handling cross-lingual summarization tasks for low-resource languages. Currently, instruction-tuned LLMs excel at various English tasks, but their performance remains unsatisfactory even with few-shot settings on CLS tasks for languages like Chinese or Spanish. To resolve this question, the authors propose a four-step zero-shot method called Summarization, Improvement, Translation and Refinement (SITR) with correspondingly designed prompts. The proposed method is tested with multiple LLMs on two well-known cross-lingual summarization datasets with various low-resource target languages. The results show that GPT-3.5 and GPT-4 significantly outperform other baselines when using the SITR method, unlocking the potential of LLMs in effectively handling cross-lingual summarization tasks for relatively low-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models can summarize texts in languages that don’t have much data. Right now, these models are really good at doing things like answering questions and generating text in English, but they struggle when it comes to summarizing texts in other languages. The authors want to know if there’s a way to make these models better at this task. They come up with a new method that uses prompts to help the models do a better job. They test their method using different language models and datasets. The results show that some of these models can actually do a good job summarizing texts in low-resource languages. |
Keywords
» Artificial intelligence » Few shot » Gpt » Summarization » Translation » Zero shot
