Summary of Llms Are Few-shot In-context Low-resource Language Learners, by Samuel Cahyawijaya et al.
LLMs Are Few-Shot In-Context Low-Resource Language Learners
by Samuel Cahyawijaya, Holy Lovenia, Pascale Fung
First submitted to arxiv on: 25 Mar 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 research paper explores the potential of in-context learning (ICL) for large language models (LLMs) to perform diverse tasks in underrepresented languages. The study focuses on low-resource languages, which are often overlooked in favor of more well-known languages like French and Spanish. To achieve this goal, the researchers examine ICL’s cross-lingual variation (X-ICL) across 25 low-resource and 7 higher-resource languages. By assessing the effectiveness of ICL with LLMs in low-resource languages and identifying its shortcomings, the study proposes a more effective alternative: query alignment. The findings highlight the significance of few-shot in-context information for enhancing the understanding quality of LLMs, which is crucial for closing the language gap between target and high-resource languages. By advancing ICL research, particularly for low-resource languages, this work underscores the importance of bridging the language divide. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to help large language models learn new things in languages that aren’t as widely spoken. Currently, most research focuses on popular languages like French and Spanish, leaving others behind. The researchers investigate a technique called in-context learning (ICL) and how it works across 32 different languages, including some less well-known ones. They find that ICL is effective for these languages but also has some limitations. To improve things, they propose a new method called query alignment. Overall, the study shows that with a little bit of information, language models can learn to understand low-resource languages more effectively. |
Keywords
* Artificial intelligence * Alignment * Few shot
